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2023-05-16T00:00:00 | 2305.08275 | ULIP-2: Towards Scalable Multimodal Pre-training For 3D Understanding | [
"Le Xue",
"Ning Yu",
"Shu Zhang",
"Junnan Li",
"Roberto Martín-Martín",
"Jiajun Wu",
"Caiming Xiong",
"Ran Xu",
"Juan Carlos Niebles",
"Silvio Savarese"
] | https://github.com/salesforce/ULIP | Recent advancements in multimodal pre-training methods have shown promising efficacy in 3D representation learning by aligning features across 3D modality, their 2D counterpart modality, and corresponding language modality. However, the methods used by existing multimodal pre-training frameworks to gather multimodal data for 3D applications lack scalability and comprehensiveness, potentially constraining the full potential of multimodal learning. The main bottleneck lies in the language modality's scalability and comprehensiveness. To address this bottleneck, we introduce ULIP-2, a multimodal pre-training framework that leverages state-of-the-art multimodal large language models (LLMs) pre-trained on extensive knowledge to automatically generate holistic language counterparts for 3D objects. We conduct experiments on two large-scale datasets, Objaverse and ShapeNet55, and release our generated three-modality triplet datasets (3D Point Cloud - Image - Language), named "ULIP-Objaverse Triplets" and "ULIP-ShapeNet Triplets". ULIP-2 requires only 3D data itself and eliminates the need for any manual annotation effort, demonstrating its scalability; and ULIP-2 achieves remarkable improvements on downstream zero-shot classification on ModelNet40 (74% Top1 Accuracy). Moreover, ULIP-2 sets a new record on the real-world ScanObjectNN benchmark (91.5% Overall Accuracy) while utilizing only 1.4 million parameters(~10x fewer than current SOTA), signifying a breakthrough in scalable multimodal 3D representation learning without human annotations. The code and datasets are available at https://github.com/salesforce/ULIP. |
2023-05-16T00:00:00 | 2305.07804 | Dr. LLaMA: Improving Small Language Models in Domain-Specific QA via Generative Data Augmentation | [
"Zhen Guo",
"Peiqi Wang",
"Yanwei Wang",
"Shangdi Yu"
] | Large Language Models (LLMs) have made significant strides in natural language processing but face challenges in terms of computational expense and inefficiency as they grow in size, especially in domain-specific tasks. Small Language Models (SLMs), on the other hand, often struggle in these tasks due to limited capacity and training data. In this paper, we introduce Dr. LLaMA, a method for improving SLMs through generative data augmentation using LLMs, focusing on medical question-answering tasks and the PubMedQA dataset. Our findings indicate that LLMs effectively refine and diversify existing question-answer pairs, resulting in improved performance of a much smaller model on domain-specific QA datasets after fine-tuning. This study highlights the challenges of using LLMs for domain-specific question answering and suggests potential research directions to address these limitations, ultimately aiming to create more efficient and capable models for specialized applications. We have also made our code available for interested researchers |
|
2023-05-16T00:00:00 | 2305.07677 | Masked Audio Text Encoders are Effective Multi-Modal Rescorers | [
"Jinglun Cai",
"Monica Sunkara",
"Xilai Li",
"Anshu Bhatia",
"Xiao Pan",
"Sravan Bodapati"
] | Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems. In this work, we propose Masked Audio Text Encoder (MATE), a multi-modal masked language model rescorer which incorporates acoustic representations into the input space of MLM. We adopt contrastive learning for effectively aligning the modalities by learning shared representations. We show that using a multi-modal rescorer is beneficial for domain generalization of the ASR system when target domain data is unavailable. MATE reduces word error rate (WER) by 4%-16% on in-domain, and 3%-7% on out-of-domain datasets, over the text-only baseline. Additionally, with very limited amount of training data (0.8 hours), MATE achieves a WER reduction of 8%-23% over the first-pass baseline. |
|
2023-05-16T00:00:00 | 2305.08844 | RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs | [
"Afra Feyza Akyürek",
"Ekin Akyürek",
"Aman Madaan",
"Ashwin Kalyan",
"Peter Clark",
"Derry Wijaya",
"Niket Tandon"
] | Despite their unprecedented success, even the largest language models make mistakes. Similar to how humans learn and improve using feedback, previous work proposed providing language models with natural language feedback to guide them in repairing their outputs. Because human-generated critiques are expensive to obtain, researchers have devised learned critique generators in lieu of human critics while assuming one can train downstream models to utilize generated feedback. However, this approach does not apply to black-box or limited access models such as ChatGPT, as they cannot be fine-tuned. Moreover, in the era of large general-purpose language agents, fine-tuning is neither computationally nor spatially efficient as it results in multiple copies of the network. In this work, we introduce RL4F (Reinforcement Learning for Feedback), a multi-agent collaborative framework where the critique generator is trained to maximize end-task performance of GPT-3, a fixed model more than 200 times its size. RL4F produces critiques that help GPT-3 revise its outputs. We study three datasets for action planning, summarization and alphabetization and show improvements (~5% on average) in multiple text similarity metrics over strong baselines across all three tasks. |
|
2023-05-16T00:00:00 | 2305.07969 | GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content | [
"Yutian Chen",
"Hao Kang",
"Vivian Zhai",
"Liangze Li",
"Rita Singh",
"Bhiksha Ramakrishnan"
] | This paper presents a novel approach for detecting ChatGPT-generated vs. human-written text using language models. To this end, we first collected and released a pre-processed dataset named OpenGPTText, which consists of rephrased content generated using ChatGPT. We then designed, implemented, and trained two different models for text classification, using Robustly Optimized BERT Pretraining Approach (RoBERTa) and Text-to-Text Transfer Transformer (T5), respectively. Our models achieved remarkable results, with an accuracy of over 97% on the test dataset, as evaluated through various metrics. Furthermore, we conducted an interpretability study to showcase our model's ability to extract and differentiate key features between human-written and ChatGPT-generated text. Our findings provide important insights into the effective use of language models to detect generated text. |
|
2023-05-16T00:00:00 | 2305.08848 | Small Models are Valuable Plug-ins for Large Language Models | [
"Canwen Xu",
"Yichong Xu",
"Shuohang Wang",
"Yang Liu",
"Chenguang Zhu",
"Julian McAuley"
] | Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned with common hardware. As a result, effectively tuning these models with large-scale supervised data can be challenging. As an alternative, In-Context Learning (ICL) can only use a small number of supervised examples due to context length limits. In this paper, we propose Super In-Context Learning (SuperICL) which allows black-box LLMs to work with locally fine-tuned smaller models, resulting in superior performance on supervised tasks. Our experiments demonstrate that SuperICL can improve performance beyond state-of-the-art fine-tuned models while addressing the instability problem of in-context learning. Furthermore, SuperICL can enhance the capabilities of smaller models, such as multilinguality and interpretability. |
|
2023-05-16T00:00:00 | 2305.08850 | Make-A-Protagonist: Generic Video Editing with An Ensemble of Experts | [
"Yuyang Zhao",
"Enze Xie",
"Lanqing Hong",
"Zhenguo Li",
"Gim Hee Lee"
] | The text-driven image and video diffusion models have achieved unprecedented success in generating realistic and diverse content. Recently, the editing and variation of existing images and videos in diffusion-based generative models have garnered significant attention. However, previous works are limited to editing content with text or providing coarse personalization using a single visual clue, rendering them unsuitable for indescribable content that requires fine-grained and detailed control. In this regard, we propose a generic video editing framework called Make-A-Protagonist, which utilizes textual and visual clues to edit videos with the goal of empowering individuals to become the protagonists. Specifically, we leverage multiple experts to parse source video, target visual and textual clues, and propose a visual-textual-based video generation model that employs mask-guided denoising sampling to generate the desired output. Extensive results demonstrate the versatile and remarkable editing capabilities of Make-A-Protagonist. |
|
2023-05-16T00:00:00 | 2305.08379 | TESS: Text-to-Text Self-Conditioned Simplex Diffusion | [
"Rabeeh Karimi Mahabadi",
"Jaesung Tae",
"Hamish Ivison",
"James Henderson",
"Iz Beltagy",
"Matthew E. Peters",
"Arman Cohan"
] | Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various domains with continuous-valued inputs. Despite the promises of fully non-autoregressive text generation, applying diffusion models to natural language remains challenging due to its discrete nature. In this work, we propose Text-to-text Self-conditioned Simplex Diffusion (TESS), a text diffusion model that is fully non-autoregressive, employs a new form of self-conditioning, and applies the diffusion process on the logit simplex space rather than the typical learned embedding space. Through extensive experiments on natural language understanding and generation tasks including summarization, text simplification, paraphrase generation, and question generation, we demonstrate that TESS outperforms state-of-the-art non-autoregressive models and is competitive with pretrained autoregressive sequence-to-sequence models. |
|
2023-05-17T00:00:00 | 2305.09641 | FitMe: Deep Photorealistic 3D Morphable Model Avatars | [
"Alexandros Lattas",
"Stylianos Moschoglou",
"Stylianos Ploumpis",
"Baris Gecer",
"Jiankang Deng",
"Stefanos Zafeiriou"
] | In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering optimization pipeline, that can be used to acquire high-fidelity renderable human avatars from single or multiple images. The model consists of a multi-modal style-based generator, that captures facial appearance in terms of diffuse and specular reflectance, and a PCA-based shape model. We employ a fast differentiable rendering process that can be used in an optimization pipeline, while also achieving photorealistic facial shading. Our optimization process accurately captures both the facial reflectance and shape in high-detail, by exploiting the expressivity of the style-based latent representation and of our shape model. FitMe achieves state-of-the-art reflectance acquisition and identity preservation on single "in-the-wild" facial images, while it produces impressive scan-like results, when given multiple unconstrained facial images pertaining to the same identity. In contrast with recent implicit avatar reconstructions, FitMe requires only one minute and produces relightable mesh and texture-based avatars, that can be used by end-user applications. |
|
2023-05-17T00:00:00 | 2305.09617 | Towards Expert-Level Medical Question Answering with Large Language Models | [
"Karan Singhal",
"Tao Tu",
"Juraj Gottweis",
"Rory Sayres",
"Ellery Wulczyn",
"Le Hou",
"Kevin Clark",
"Stephen Pfohl",
"Heather Cole-Lewis",
"Darlene Neal",
"Mike Schaekermann",
"Amy Wang",
"Mohamed Amin",
"Sami Lachgar",
"Philip Mansfield",
"Sushant Prakash",
"Bradley Green",
"Ewa Dominowska",
"Blaise Aguera y Arcas",
"Nenad Tomasev",
"Yun Liu",
"Renee Wong",
"Christopher Semturs",
"S. Sara Mahdavi",
"Joelle Barral",
"Dale Webster",
"Greg S. Corrado",
"Yossi Matias",
"Shekoofeh Azizi",
"Alan Karthikesalingam",
"Vivek Natarajan"
] | Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason over it, and answer medical questions comparably to physicians has long been viewed as one such grand challenge. Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a "passing" score in US Medical Licensing Examination (USMLE) style questions with a score of 67.2% on the MedQA dataset. However, this and other prior work suggested significant room for improvement, especially when models' answers were compared to clinicians' answers. Here we present Med-PaLM 2, which bridges these gaps by leveraging a combination of base LLM improvements (PaLM 2), medical domain finetuning, and prompting strategies including a novel ensemble refinement approach. Med-PaLM 2 scored up to 86.5% on the MedQA dataset, improving upon Med-PaLM by over 19% and setting a new state-of-the-art. We also observed performance approaching or exceeding state-of-the-art across MedMCQA, PubMedQA, and MMLU clinical topics datasets. We performed detailed human evaluations on long-form questions along multiple axes relevant to clinical applications. In pairwise comparative ranking of 1066 consumer medical questions, physicians preferred Med-PaLM 2 answers to those produced by physicians on eight of nine axes pertaining to clinical utility (p < 0.001). We also observed significant improvements compared to Med-PaLM on every evaluation axis (p < 0.001) on newly introduced datasets of 240 long-form "adversarial" questions to probe LLM limitations. While further studies are necessary to validate the efficacy of these models in real-world settings, these results highlight rapid progress towards physician-level performance in medical question answering. |
|
2023-05-17T00:00:00 | 2305.09137 | Pre-Training to Learn in Context | [
"Yuxian Gu",
"Li Dong",
"Furu Wei",
"Minlie Huang"
] | https://github.com/thu-coai/PICL | In-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP community. However, the ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. To this end, we propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability by pre-training the model on a large collection of "intrinsic tasks" in the general plain-text corpus using the simple language modeling objective. PICL encourages the model to infer and perform tasks by conditioning on the contexts while maintaining task generalization of pre-trained models. We evaluate the in-context learning performance of the model trained with PICL on seven widely-used text classification datasets and the Super-NaturalInstrctions benchmark, which contains 100+ NLP tasks formulated to text generation. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters. The code is publicly available at https://github.com/thu-coai/PICL. |
2023-05-17T00:00:00 | 2305.09636 | SoundStorm: Efficient Parallel Audio Generation | [
"Zalán Borsos",
"Matt Sharifi",
"Damien Vincent",
"Eugene Kharitonov",
"Neil Zeghidour",
"Marco Tagliasacchi"
] | We present SoundStorm, a model for efficient, non-autoregressive audio generation. SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional attention and confidence-based parallel decoding to generate the tokens of a neural audio codec. Compared to the autoregressive generation approach of AudioLM, our model produces audio of the same quality and with higher consistency in voice and acoustic conditions, while being two orders of magnitude faster. SoundStorm generates 30 seconds of audio in 0.5 seconds on a TPU-v4. We demonstrate the ability of our model to scale audio generation to longer sequences by synthesizing high-quality, natural dialogue segments, given a transcript annotated with speaker turns and a short prompt with the speakers' voices. |
|
2023-05-17T00:00:00 | 2305.08891 | Common Diffusion Noise Schedules and Sample Steps are Flawed | [
"Shanchuan Lin",
"Bingchen Liu",
"Jiashi Li",
"Xiao Yang"
] | We discover that common diffusion noise schedules do not enforce the last timestep to have zero signal-to-noise ratio (SNR), and some implementations of diffusion samplers do not start from the last timestep. Such designs are flawed and do not reflect the fact that the model is given pure Gaussian noise at inference, creating a discrepancy between training and inference. We show that the flawed design causes real problems in existing implementations. In Stable Diffusion, it severely limits the model to only generate images with medium brightness and prevents it from generating very bright and dark samples. We propose a few simple fixes: (1) rescale the noise schedule to enforce zero terminal SNR; (2) train the model with v prediction; (3) change the sampler to always start from the last timestep; (4) rescale classifier-free guidance to prevent over-exposure. These simple changes ensure the diffusion process is congruent between training and inference and allow the model to generate samples more faithful to the original data distribution. |
|
2023-05-17T00:00:00 | 2305.09253 | Online Continual Learning Without the Storage Constraint | [
"Ameya Prabhu",
"Zhipeng Cai",
"Puneet Dokania",
"Philip Torr",
"Vladlen Koltun",
"Ozan Sener"
] | https://github.com/drimpossible/ACM | Online continual learning (OCL) research has primarily focused on mitigating catastrophic forgetting with fixed and limited storage allocation throughout the agent's lifetime. However, the growing affordability of data storage highlights a broad range of applications that do not adhere to these assumptions. In these cases, the primary concern lies in managing computational expenditures rather than storage. In this paper, we target such settings, investigating the online continual learning problem by relaxing storage constraints and emphasizing fixed, limited economical budget. We provide a simple algorithm that can compactly store and utilize the entirety of the incoming data stream under tiny computational budgets using a kNN classifier and universal pre-trained feature extractors. Our algorithm provides a consistency property attractive to continual learning: It will never forget past seen data. We set a new state of the art on two large-scale OCL datasets: Continual LOCalization (CLOC), which has 39M images over 712 classes, and Continual Google Landmarks V2 (CGLM), which has 580K images over 10,788 classes -- beating methods under far higher computational budgets than ours in terms of both reducing catastrophic forgetting of past data and quickly adapting to rapidly changing data streams. We provide code to reproduce our results at https://github.com/drimpossible/ACM. |
2023-05-17T00:00:00 | 2305.09664 | Understanding 3D Object Interaction from a Single Image | [
"Shengyi Qian",
"David F. Fouhey"
] | Humans can easily understand a single image as depicting multiple potential objects permitting interaction. We use this skill to plan our interactions with the world and accelerate understanding new objects without engaging in interaction. In this paper, we would like to endow machines with the similar ability, so that intelligent agents can better explore the 3D scene or manipulate objects. Our approach is a transformer-based model that predicts the 3D location, physical properties and affordance of objects. To power this model, we collect a dataset with Internet videos, egocentric videos and indoor images to train and validate our approach. Our model yields strong performance on our data, and generalizes well to robotics data. |
|
2023-05-17T00:00:00 | 2305.09148 | Dual-Alignment Pre-training for Cross-lingual Sentence Embedding | [
"Ziheng Li",
"Shaohan Huang",
"Zihan Zhang",
"Zhi-Hong Deng",
"Qiang Lou",
"Haizhen Huang",
"Jian Jiao",
"Furu Wei",
"Weiwei Deng",
"Qi Zhang"
] | https://github.com/ChillingDream/DAP | Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. However, our research indicates that token-level alignment is also crucial in multilingual scenarios, which has not been fully explored previously. Based on our findings, we propose a dual-alignment pre-training (DAP) framework for cross-lingual sentence embedding that incorporates both sentence-level and token-level alignment. To achieve this, we introduce a novel representation translation learning (RTL) task, where the model learns to use one-side contextualized token representation to reconstruct its translation counterpart. This reconstruction objective encourages the model to embed translation information into the token representation. Compared to other token-level alignment methods such as translation language modeling, RTL is more suitable for dual encoder architectures and is computationally efficient. Extensive experiments on three sentence-level cross-lingual benchmarks demonstrate that our approach can significantly improve sentence embedding. Our code is available at https://github.com/ChillingDream/DAP. |
2023-05-17T00:00:00 | 2305.09515 | AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation | [
"Tong Wu",
"Zhihao Fan",
"Xiao Liu",
"Yeyun Gong",
"Yelong Shen",
"Jian Jiao",
"Hai-Tao Zheng",
"Juntao Li",
"Zhongyu Wei",
"Jian Guo",
"Nan Duan",
"Weizhu Chen"
] | Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generation via generating all tokens within a sequence concurrently. However, natural language exhibits a far more pronounced sequential dependency in comparison to images, and the majority of existing language models are trained utilizing a left-to-right auto-regressive approach. To account for the inherent sequential characteristic of natural language, we introduce Auto-Regressive Diffusion (AR-Diffusion). AR-Diffusion ensures that the generation of tokens on the right depends on the generated ones on the left, a mechanism achieved through employing a dynamic number of denoising steps that vary based on token position. This results in tokens on the left undergoing fewer denoising steps than those on the right, thereby enabling them to generate earlier and subsequently influence the generation of tokens on the right. In a series of experiments on various text generation tasks including text summarization, machine translation, and common sense generation, AR-Diffusion clearly demonstrated the superiority over existing diffusion language models and that it can be 100timessim600times faster when achieving comparable results. Our code will be publicly released. |
|
2023-05-17T00:00:00 | 2305.09662 | Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation | [
"Samaneh Azadi",
"Akbar Shah",
"Thomas Hayes",
"Devi Parikh",
"Sonal Gupta"
] | Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models for motion generation has enabled improvements in the quality of generated motions. However, existing approaches are limited by their reliance on relatively small-scale motion capture data, leading to poor performance on more diverse, in-the-wild prompts. In this paper, we introduce Make-An-Animation, a text-conditioned human motion generation model which learns more diverse poses and prompts from large-scale image-text datasets, enabling significant improvement in performance over prior works. Make-An-Animation is trained in two stages. First, we train on a curated large-scale dataset of (text, static pseudo-pose) pairs extracted from image-text datasets. Second, we fine-tune on motion capture data, adding additional layers to model the temporal dimension. Unlike prior diffusion models for motion generation, Make-An-Animation uses a U-Net architecture similar to recent text-to-video generation models. Human evaluation of motion realism and alignment with input text shows that our model reaches state-of-the-art performance on text-to-motion generation. |
|
2023-05-18T00:00:00 | 2305.09857 | CoEdIT: Text Editing by Task-Specific Instruction Tuning | [
"Vipul Raheja",
"Dhruv Kumar",
"Ryan Koo",
"Dongyeop Kang"
] | Text editing or revision is an essential function of the human writing process. Understanding the capabilities of LLMs for making high-quality revisions and collaborating with human writers is a critical step toward building effective writing assistants. With the prior success of LLMs and instruction tuning, we leverage instruction-tuned LLMs for text revision to improve the quality of user-generated text and improve the efficiency of the process. We introduce CoEdIT, a state-of-the-art text editing model for writing assistance. CoEdIT takes instructions from the user specifying the attributes of the desired text, such as "Make the sentence simpler" or "Write it in a more neutral style," and outputs the edited text. We present a large language model fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions). Our model (1) achieves state-of-the-art performance on various text editing benchmarks, (2) is competitive with publicly available largest-sized LLMs trained on instructions while being sim60x smaller, (3) is capable of generalizing to unseen edit instructions, and (4) exhibits compositional comprehension abilities to generalize to instructions containing different combinations of edit actions. Through extensive qualitative and quantitative analysis, we show that writers prefer the edits suggested by CoEdIT, relative to other state-of-the-art text editing models. Our code and dataset are publicly available. |
|
2023-05-18T00:00:00 | 2305.10425 | SLiC-HF: Sequence Likelihood Calibration with Human Feedback | [
"Yao Zhao",
"Rishabh Joshi",
"Tianqi Liu",
"Misha Khalman",
"Mohammad Saleh",
"Peter J. Liu"
] | Learning from human feedback has been shown to be effective at aligning language models with human preferences. Past work has often relied on Reinforcement Learning from Human Feedback (RLHF), which optimizes the language model using reward scores assigned from a reward model trained on human preference data. In this work we show how the recently introduced Sequence Likelihood Calibration (SLiC), can also be used to effectively learn from human preferences (SLiC-HF). Furthermore, we demonstrate this can be done with human feedback data collected for a different model, similar to off-policy, offline RL data. Automatic and human evaluation experiments on the TL;DR summarization task show that SLiC-HF significantly improves supervised fine-tuning baselines. Furthermore, SLiC-HF presents a competitive alternative to the PPO RLHF implementation used in past work while being much simpler to implement, easier to tune and more computationally efficient in practice. |
|
2023-05-18T00:00:00 | 2305.09863 | Explaining black box text modules in natural language with language models | [
"Chandan Singh",
"Aliyah R. Hsu",
"Richard Antonello",
"Shailee Jain",
"Alexander G. Huth",
"Bin Yu",
"Jianfeng Gao"
] | Large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their rapid proliferation and increasing opaqueness have created a growing need for interpretability. Here, we ask whether we can automatically obtain natural language explanations for black box text modules. A "text module" is any function that maps text to a scalar continuous value, such as a submodule within an LLM or a fitted model of a brain region. "Black box" indicates that we only have access to the module's inputs/outputs. We introduce Summarize and Score (SASC), a method that takes in a text module and returns a natural language explanation of the module's selectivity along with a score for how reliable the explanation is. We study SASC in 3 contexts. First, we evaluate SASC on synthetic modules and find that it often recovers ground truth explanations. Second, we use SASC to explain modules found within a pre-trained BERT model, enabling inspection of the model's internals. Finally, we show that SASC can generate explanations for the response of individual fMRI voxels to language stimuli, with potential applications to fine-grained brain mapping. All code for using SASC and reproducing results is made available on Github. |
|
2023-05-18T00:00:00 | 2305.10403 | PaLM 2 Technical Report | [
"Rohan Anil",
"Andrew M. Dai",
"Orhan Firat",
"Melvin Johnson",
"Dmitry Lepikhin",
"Alexandre Passos",
"Siamak Shakeri",
"Emanuel Taropa",
"Paige Bailey",
"Zhifeng Chen",
"Eric Chu",
"Jonathan H. Clark",
"Laurent El Shafey",
"Yanping Huang",
"Kathy Meier-Hellstern",
"Gaurav Mishra",
"Erica Moreira",
"Mark Omernick",
"Kevin Robinson",
"Sebastian Ruder",
"Yi Tay",
"Kefan Xiao",
"Yuanzhong Xu",
"Yujing Zhang",
"Gustavo Hernandez Abrego",
"Junwhan Ahn",
"Jacob Austin",
"Paul Barham",
"Jan Botha",
"James Bradbury",
"Siddhartha Brahma",
"Kevin Brooks",
"Michele Catasta",
"Yong Cheng",
"Colin Cherry",
"Christopher A. Choquette-Choo",
"Aakanksha Chowdhery",
"Clément Crepy",
"Shachi Dave",
"Mostafa Dehghani",
"Sunipa Dev",
"Jacob Devlin",
"Mark Díaz",
"Nan Du",
"Ethan Dyer",
"Vlad Feinberg",
"Fangxiaoyu Feng",
"Vlad Fienber",
"Markus Freitag",
"Xavier Garcia",
"Sebastian Gehrmann",
"Lucas Gonzalez",
"Guy Gur-Ari",
"Steven Hand",
"Hadi Hashemi",
"Le Hou",
"Joshua Howland",
"Andrea Hu",
"Jeffrey Hui",
"Jeremy Hurwitz",
"Michael Isard",
"Abe Ittycheriah",
"Matthew Jagielski",
"Wenhao Jia",
"Kathleen Kenealy",
"Maxim Krikun",
"Sneha Kudugunta",
"Chang Lan",
"Katherine Lee",
"Benjamin Lee",
"Eric Li",
"Music Li",
"Wei Li",
"YaGuang Li",
"Jian Li",
"Hyeontaek Lim",
"Hanzhao Lin",
"Zhongtao Liu",
"Frederick Liu",
"Marcello Maggioni",
"Aroma Mahendru",
"Joshua Maynez",
"Vedant Misra",
"Maysam Moussalem",
"Zachary Nado",
"John Nham",
"Eric Ni",
"Andrew Nystrom",
"Alicia Parrish",
"Marie Pellat",
"Martin Polacek",
"Alex Polozov",
"Reiner Pope",
"Siyuan Qiao",
"Emily Reif",
"Bryan Richter",
"Parker Riley",
"Alex Castro Ros",
"Aurko Roy",
"Brennan Saeta",
"Rajkumar Samuel",
"Renee Shelby",
"Ambrose Slone",
"Daniel Smilkov",
"David R. So",
"Daniel Sohn",
"Simon Tokumine",
"Dasha Valter",
"Vijay Vasudevan",
"Kiran Vodrahalli",
"Xuezhi Wang",
"Pidong Wang",
"Zirui Wang",
"Tao Wang",
"John Wieting",
"Yuhuai Wu",
"Kelvin Xu",
"Yunhan Xu",
"Linting Xue",
"Pengcheng Yin",
"Jiahui Yu",
"Qiao Zhang",
"Steven Zheng",
"Ce Zheng",
"Weikang Zhou",
"Denny Zhou",
"Slav Petrov",
"Yonghui Wu"
] | We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report. |
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2023-05-18T00:00:00 | 2305.09975 | Smart Word Suggestions for Writing Assistance | [
"Chenshuo Wang",
"Shaoguang Mao",
"Tao Ge",
"Wenshan Wu",
"Xun Wang",
"Yan Xia",
"Jonathan Tien",
"Dongyan Zhao"
] | https://github.com/microsoft/SmartWordSuggestions | Enhancing word usage is a desired feature for writing assistance. To further advance research in this area, this paper introduces "Smart Word Suggestions" (SWS) task and benchmark. Unlike other works, SWS emphasizes end-to-end evaluation and presents a more realistic writing assistance scenario. This task involves identifying words or phrases that require improvement and providing substitution suggestions. The benchmark includes human-labeled data for testing, a large distantly supervised dataset for training, and the framework for evaluation. The test data includes 1,000 sentences written by English learners, accompanied by over 16,000 substitution suggestions annotated by 10 native speakers. The training dataset comprises over 3.7 million sentences and 12.7 million suggestions generated through rules. Our experiments with seven baselines demonstrate that SWS is a challenging task. Based on experimental analysis, we suggest potential directions for future research on SWS. The dataset and related codes is available at https://github.com/microsoft/SmartWordSuggestions. |
2023-05-18T00:00:00 | 2305.09761 | NerfBridge: Bringing Real-time, Online Neural Radiance Field Training to Robotics | [
"Javier Yu",
"Jun En Low",
"Keiko Nagami",
"Mac Schwager"
] | https://github.com/javieryu/nerf_bridge | This work was presented at the IEEE International Conference on Robotics and Automation 2023 Workshop on Unconventional Spatial Representations. Neural radiance fields (NeRFs) are a class of implicit scene representations that model 3D environments from color images. NeRFs are expressive, and can model the complex and multi-scale geometry of real world environments, which potentially makes them a powerful tool for robotics applications. Modern NeRF training libraries can generate a photo-realistic NeRF from a static data set in just a few seconds, but are designed for offline use and require a slow pose optimization pre-computation step. In this work we propose NerfBridge, an open-source bridge between the Robot Operating System (ROS) and the popular Nerfstudio library for real-time, online training of NeRFs from a stream of images. NerfBridge enables rapid development of research on applications of NeRFs in robotics by providing an extensible interface to the efficient training pipelines and model libraries provided by Nerfstudio. As an example use case we outline a hardware setup that can be used NerfBridge to train a NeRF from images captured by a camera mounted to a quadrotor in both indoor and outdoor environments. For accompanying video https://youtu.be/EH0SLn-RcDg and code https://github.com/javieryu/nerf_bridge. |
2023-05-18T00:00:00 | 2305.10320 | CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo | [
"Weitao Chen",
"Hongbin Xu",
"Zhipeng Zhou",
"Yang Liu",
"Baigui Sun",
"Wenxiong Kang",
"Xuansong Xie"
] | The core of Multi-view Stereo(MVS) is the matching process among reference and source pixels. Cost aggregation plays a significant role in this process, while previous methods focus on handling it via CNNs. This may inherit the natural limitation of CNNs that fail to discriminate repetitive or incorrect matches due to limited local receptive fields. To handle the issue, we aim to involve Transformer into cost aggregation. However, another problem may occur due to the quadratically growing computational complexity caused by Transformer, resulting in memory overflow and inference latency. In this paper, we overcome these limits with an efficient Transformer-based cost aggregation network, namely CostFormer. The Residual Depth-Aware Cost Transformer(RDACT) is proposed to aggregate long-range features on cost volume via self-attention mechanisms along the depth and spatial dimensions. Furthermore, Residual Regression Transformer(RRT) is proposed to enhance spatial attention. The proposed method is a universal plug-in to improve learning-based MVS methods. |
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2023-05-18T00:00:00 | 2305.10018 | Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers | [
"Manuel Lagunas",
"Brayan Impata",
"Victor Martinez",
"Virginia Fernandez",
"Christos Georgakis",
"Sofia Braun",
"Felipe Bertrand"
] | Fine-grained classification is a challenging task that involves identifying subtle differences between objects within the same category. This task is particularly challenging in scenarios where data is scarce. Visual transformers (ViT) have recently emerged as a powerful tool for image classification, due to their ability to learn highly expressive representations of visual data using self-attention mechanisms. In this work, we explore Semi-ViT, a ViT model fine tuned using semi-supervised learning techniques, suitable for situations where we have lack of annotated data. This is particularly common in e-commerce, where images are readily available but labels are noisy, nonexistent, or expensive to obtain. Our results demonstrate that Semi-ViT outperforms traditional convolutional neural networks (CNN) and ViTs, even when fine-tuned with limited annotated data. These findings indicate that Semi-ViTs hold significant promise for applications that require precise and fine-grained classification of visual data. |
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2023-05-18T00:00:00 | 2305.10429 | DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining | [
"Sang Michael Xie",
"Hieu Pham",
"Xuanyi Dong",
"Nan Du",
"Hanxiao Liu",
"Yifeng Lu",
"Percy Liang",
"Quoc V. Le",
"Tengyu Ma",
"Adams Wei Yu"
] | The mixture proportions of pretraining data domains (e.g., Wikipedia, books, web text) greatly affect language model (LM) performance. In this paper, we propose Domain Reweighting with Minimax Optimization (DoReMi), which first trains a small proxy model using group distributionally robust optimization (Group DRO) over domains to produce domain weights (mixture proportions) without knowledge of downstream tasks. We then resample a dataset with these domain weights and train a larger, full-sized model. In our experiments, we use DoReMi on a 280M-parameter proxy model to find domain weights for training an 8B-parameter model (30x larger) more efficiently. On The Pile, DoReMi improves perplexity across all domains, even when it downweights a domain. DoReMi improves average few-shot downstream accuracy by 6.5% over a baseline model trained using The Pile's default domain weights and reaches the baseline accuracy with 2.6x fewer training steps. On the GLaM dataset, DoReMi, which has no knowledge of downstream tasks, even matches the performance of using domain weights tuned on downstream tasks. |
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2023-05-18T00:00:00 | 2305.10266 | Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability | [
"Eleftheria Briakou",
"Colin Cherry",
"George Foster"
] | Large, multilingual language models exhibit surprisingly good zero- or few-shot machine translation capabilities, despite having never seen the intentionally-included translation examples provided to typical neural translation systems. We investigate the role of incidental bilingualism -- the unintentional consumption of bilingual signals, including translation examples -- in explaining the translation capabilities of large language models, taking the Pathways Language Model (PaLM) as a case study. We introduce a mixed-method approach to measure and understand incidental bilingualism at scale. We show that PaLM is exposed to over 30 million translation pairs across at least 44 languages. Furthermore, the amount of incidental bilingual content is highly correlated with the amount of monolingual in-language content for non-English languages. We relate incidental bilingual content to zero-shot prompts and show that it can be used to mine new prompts to improve PaLM's out-of-English zero-shot translation quality. Finally, in a series of small-scale ablations, we show that its presence has a substantial impact on translation capabilities, although this impact diminishes with model scale. |
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2023-05-18T00:00:00 | 2305.10142 | Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback | [
"Yao Fu",
"Hao Peng",
"Tushar Khot",
"Mirella Lapata"
] | We study whether multiple large language models (LLMs) can autonomously improve each other in a negotiation game by playing, reflecting, and criticizing. We are interested in this question because if LLMs were able to improve each other, it would imply the possibility of creating strong AI agents with minimal human intervention. We ask two LLMs to negotiate with each other, playing the roles of a buyer and a seller, respectively. They aim to reach a deal with the buyer targeting a lower price and the seller a higher one. A third language model, playing the critic, provides feedback to a player to improve the player's negotiation strategies. We let the two agents play multiple rounds, using previous negotiation history and AI feedback as in-context demonstrations to improve the model's negotiation strategy iteratively. We use different LLMs (GPT and Claude) for different roles and use the deal price as the evaluation metric. Our experiments reveal multiple intriguing findings: (1) Only a subset of the language models we consider can self-play and improve the deal price from AI feedback, weaker models either do not understand the game's rules or cannot incorporate AI feedback for further improvement. (2) Models' abilities to learn from the feedback differ when playing different roles. For example, it is harder for Claude-instant to improve as the buyer than as the seller. (3) When unrolling the game to multiple rounds, stronger agents can consistently improve their performance by meaningfully using previous experiences and iterative AI feedback, yet have a higher risk of breaking the deal. We hope our work provides insightful initial explorations of having models autonomously improve each other with game playing and AI feedback. |
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2023-05-18T00:00:00 | 2305.10005 | DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning | [
"Alexander H. Liu",
"Heng-Jui Chang",
"Michael Auli",
"Wei-Ning Hsu",
"James R. Glass"
] | In this paper, we introduce self-distillation and online clustering for self-supervised speech representation learning (DinoSR) which combines masked language modeling, self-distillation, and online clustering. We show that these concepts complement each other and result in a strong representation learning model for speech. DinoSR first extracts contextualized embeddings from the input audio with a teacher network, then runs an online clustering system on the embeddings to yield a machine-discovered phone inventory, and finally uses the discretized tokens to guide a student network. We show that DinoSR surpasses previous state-of-the-art performance in several downstream tasks, and provide a detailed analysis of the model and the learned discrete units. The source code will be made available after the anonymity period. |
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2023-05-18T00:00:00 | 2305.09764 | Application-Agnostic Language Modeling for On-Device ASR | [
"Markus Nußbaum-Thom",
"Lyan Verwimp",
"Youssef Oualil"
] | On-device automatic speech recognition systems face several challenges compared to server-based systems. They have to meet stricter constraints in terms of speed, disk size and memory while maintaining the same accuracy. Often they have to serve several applications with different distributions at once, such as communicating with a virtual assistant and speech-to-text. The simplest solution to serve multiple applications is to build application-specific (language) models, but this leads to an increase in memory. Therefore, we explore different data- and architecture-driven language modeling approaches to build a single application-agnostic model. We propose two novel feed-forward architectures that find an optimal trade off between different on-device constraints. In comparison to the application-specific solution, one of our novel approaches reduces the disk size by half, while maintaining speed and accuracy of the original model. |
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2023-05-18T00:00:00 | 2305.09758 | A Video Is Worth 4096 Tokens: Verbalize Story Videos To Understand Them In Zero Shot | [
"Aanisha Bhattacharya",
"Yaman K Singla",
"Balaji Krishnamurthy",
"Rajiv Ratn Shah",
"Changyou Chen"
] | Multimedia content, such as advertisements and story videos, exhibit a rich blend of creativity and multiple modalities. They incorporate elements like text, visuals, audio, and storytelling techniques, employing devices like emotions, symbolism, and slogans to convey meaning. While previous research in multimedia understanding has focused mainly on videos with specific actions like cooking, there is a dearth of large annotated training datasets, hindering the development of supervised learning models with satisfactory performance for real-world applications. However, the rise of large language models (LLMs) has witnessed remarkable zero-shot performance in various natural language processing (NLP) tasks, such as emotion classification, question-answering, and topic classification. To bridge this performance gap in multimedia understanding, we propose verbalizing story videos to generate their descriptions in natural language and then performing video-understanding tasks on the generated story as opposed to the original video. Through extensive experiments on five video-understanding tasks, we demonstrate that our method, despite being zero-shot, achieves significantly better results than supervised baselines for video understanding. Further, alleviating a lack of story understanding benchmarks, we publicly release the first dataset on a crucial task in computational social science, persuasion strategy identification. |
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2023-05-18T00:00:00 | 2305.10400 | What You See is What You Read? Improving Text-Image Alignment Evaluation | [
"Michal Yarom",
"Yonatan Bitton",
"Soravit Changpinyo",
"Roee Aharoni",
"Jonathan Herzig",
"Oran Lang",
"Eran Ofek",
"Idan Szpektor"
] | Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks. In this work, we study methods for automatic text-image alignment evaluation. We first introduce SeeTRUE: a comprehensive evaluation set, spanning multiple datasets from both text-to-image and image-to-text generation tasks, with human judgements for whether a given text-image pair is semantically aligned. We then describe two automatic methods to determine alignment: the first involving a pipeline based on question generation and visual question answering models, and the second employing an end-to-end classification approach by finetuning multimodal pretrained models. Both methods surpass prior approaches in various text-image alignment tasks, with significant improvements in challenging cases that involve complex composition or unnatural images. Finally, we demonstrate how our approaches can localize specific misalignments between an image and a given text, and how they can be used to automatically re-rank candidates in text-to-image generation. |
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2023-05-18T00:00:00 | 2305.10431 | FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention | [
"Guangxuan Xiao",
"Tianwei Yin",
"William T. Freeman",
"Frédo Durand",
"Song Han"
] | https://github.com/mit-han-lab/fastcomposer | Diffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers efficient deployment. Moreover, existing methods struggle with multi-subject generation as they often blend features among subjects. We present FastComposer which enables efficient, personalized, multi-subject text-to-image generation without fine-tuning. FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes. To address the identity blending problem in the multi-subject generation, FastComposer proposes cross-attention localization supervision during training, enforcing the attention of reference subjects localized to the correct regions in the target images. Naively conditioning on subject embeddings results in subject overfitting. FastComposer proposes delayed subject conditioning in the denoising step to maintain both identity and editability in subject-driven image generation. FastComposer generates images of multiple unseen individuals with different styles, actions, and contexts. It achieves 300times-2500times speedup compared to fine-tuning-based methods and requires zero extra storage for new subjects. FastComposer paves the way for efficient, personalized, and high-quality multi-subject image creation. Code, model, and dataset are available at https://github.com/mit-han-lab/fastcomposer. |
2023-05-19T00:00:00 | 2305.10973 | Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold | [
"Xingang Pan",
"Ayush Tewari",
"Thomas Leimkühler",
"Lingjie Liu",
"Abhimitra Meka",
"Christian Theobalt"
] | Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model, which often lack flexibility, precision, and generality. In this work, we study a powerful yet much less explored way of controlling GANs, that is, to "drag" any points of the image to precisely reach target points in a user-interactive manner, as shown in Fig.1. To achieve this, we propose DragGAN, which consists of two main components: 1) a feature-based motion supervision that drives the handle point to move towards the target position, and 2) a new point tracking approach that leverages the discriminative generator features to keep localizing the position of the handle points. Through DragGAN, anyone can deform an image with precise control over where pixels go, thus manipulating the pose, shape, expression, and layout of diverse categories such as animals, cars, humans, landscapes, etc. As these manipulations are performed on the learned generative image manifold of a GAN, they tend to produce realistic outputs even for challenging scenarios such as hallucinating occluded content and deforming shapes that consistently follow the object's rigidity. Both qualitative and quantitative comparisons demonstrate the advantage of DragGAN over prior approaches in the tasks of image manipulation and point tracking. We also showcase the manipulation of real images through GAN inversion. |
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2023-05-19T00:00:00 | 2305.10764 | OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding | [
"Minghua Liu",
"Ruoxi Shi",
"Kaiming Kuang",
"Yinhao Zhu",
"Xuanlin Li",
"Shizhong Han",
"Hong Cai",
"Fatih Porikli",
"Hao Su"
] | We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but with a specific focus on scaling up 3D representations to enable open-world 3D shape understanding. To achieve this, we scale up training data by ensembling multiple 3D datasets and propose several strategies to automatically filter and enrich noisy text descriptions. We also explore and compare strategies for scaling 3D backbone networks and introduce a novel hard negative mining module for more efficient training. We evaluate OpenShape on zero-shot 3D classification benchmarks and demonstrate its superior capabilities for open-world recognition. Specifically, OpenShape achieves a zero-shot accuracy of 46.8% on the 1,156-category Objaverse-LVIS benchmark, compared to less than 10% for existing methods. OpenShape also achieves an accuracy of 85.3% on ModelNet40, outperforming previous zero-shot baseline methods by 20% and performing on par with some fully-supervised methods. Furthermore, we show that our learned embeddings encode a wide range of visual and semantic concepts (e.g., subcategories, color, shape, style) and facilitate fine-grained text-3D and image-3D interactions. Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation. |
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2023-05-19T00:00:00 | 2305.10763 | CLAPSpeech: Learning Prosody from Text Context with Contrastive Language-Audio Pre-training | [
"Zhenhui Ye",
"Rongjie Huang",
"Yi Ren",
"Ziyue Jiang",
"Jinglin Liu",
"Jinzheng He",
"Xiang Yin",
"Zhou Zhao"
] | Improving text representation has attracted much attention to achieve expressive text-to-speech (TTS). However, existing works only implicitly learn the prosody with masked token reconstruction tasks, which leads to low training efficiency and difficulty in prosody modeling. We propose CLAPSpeech, a cross-modal contrastive pre-training framework that explicitly learns the prosody variance of the same text token under different contexts. Specifically, 1) We encourage the model to connect the text context with its corresponding prosody pattern in the joint multi-modal space with the elaborate design of the encoder inputs and contrastive loss; 2) We introduce a multi-scale pre-training pipeline to capture prosody patterns in multiple levels. We show how to incorporate CLAPSpeech into existing TTS models for better prosody. Experiments on three datasets not only show that CLAPSpeech could improve the prosody prediction for existing TTS methods, but also demonstrate its generalization ability to adapt to multiple languages and multi-speaker TTS. We also deeply analyze the principle behind the performance of CLAPSpeech. Ablation studies demonstrate the necessity of each component in our method. Source code and audio samples are available at https://clapspeech.github.io. |
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2023-05-19T00:00:00 | 2305.11171 | TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models | [
"Zorik Gekhman",
"Jonathan Herzig",
"Roee Aharoni",
"Chen Elkind",
"Idan Szpektor"
] | Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries. Previous work improved such models with synthetic training data. However, the data is typically based on perturbed human-written summaries, which often differ in their characteristics from real model-generated summaries and have limited coverage of possible factual errors. Alternatively, large language models (LLMs) have recently shown promising results in directly evaluating generative tasks, but are too computationally expensive for practical use. Motivated by these limitations, we introduce TrueTeacher, a method for generating synthetic data by annotating diverse model-generated summaries using a LLM. Unlike prior work, TrueTeacher does not rely on human-written summaries, and is multilingual by nature. Experiments on the TRUE benchmark show that a student model trained using our data, substantially outperforms both the state-of-the-art model with similar capacity, and the LLM teacher. In a systematic study, we compare TrueTeacher to existing synthetic data generation methods and demonstrate its superiority and robustness to domain-shift. Using the the mFACE dataset, we also show that our method generalizes to multilingual scenarios. Finally, we release a large-scale synthetic dataset with 1.4M examples generated using TrueTeacher. |
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2023-05-19T00:00:00 | 2305.10434 | Learning the Visualness of Text Using Large Vision-Language Models | [
"Gaurav Verma",
"Ryan A. Rossi",
"Christopher Tensmeyer",
"Jiuxiang Gu",
"Ani Nenkova"
] | Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will unlock the ability to augment text with relevant images, as neural text-to-image generation and retrieval models operate on the implicit assumption that the input text is visual in nature. We curate a dataset of 3,620 English sentences and their visualness scores provided by multiple human annotators. Additionally, we use documents that contain text and visual assets to create a distantly supervised corpus of document text and associated images. We also propose a fine-tuning strategy that adapts large vision-language models like CLIP that assume a one-to-one correspondence between text and image to the task of scoring text visualness from text input alone. Our strategy involves modifying the model's contrastive learning objective to map text identified as non-visual to a common NULL image while matching visual text to their corresponding images in the document. We evaluate the proposed approach on its ability to (i) classify visual and non-visual text accurately, and (ii) attend over words that are identified as visual in psycholinguistic studies. Empirical evaluation indicates that our approach performs better than several heuristics and baseline models for the proposed task. Furthermore, to highlight the importance of modeling the visualness of text, we conduct qualitative analyses of text-to-image generation systems like DALL-E. |
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2023-05-19T00:00:00 | 2305.10601 | Tree of Thoughts: Deliberate Problem Solving with Large Language Models | [
"Shunyu Yao",
"Dian Yu",
"Jeffrey Zhao",
"Izhak Shafran",
"Thomas L. Griffiths",
"Yuan Cao",
"Karthik Narasimhan"
] | https://github.com/ysymyth/tree-of-thought-llm | Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models' problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: https://github.com/ysymyth/tree-of-thought-llm. |
2023-05-19T00:00:00 | 2305.10855 | TextDiffuser: Diffusion Models as Text Painters | [
"Jingye Chen",
"Yupan Huang",
"Tengchao Lv",
"Lei Cui",
"Qifeng Chen",
"Furu Wei"
] | https://github.com/microsoft/unilm/tree/master/textdiffuser | Diffusion models have gained increasing attention for their impressive generation abilities but currently struggle with rendering accurate and coherent text. To address this issue, we introduce TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds. TextDiffuser consists of two stages: first, a Transformer model generates the layout of keywords extracted from text prompts, and then diffusion models generate images conditioned on the text prompt and the generated layout. Additionally, we contribute the first large-scale text images dataset with OCR annotations, MARIO-10M, containing 10 million image-text pairs with text recognition, detection, and character-level segmentation annotations. We further collect the MARIO-Eval benchmark to serve as a comprehensive tool for evaluating text rendering quality. Through experiments and user studies, we show that TextDiffuser is flexible and controllable to create high-quality text images using text prompts alone or together with text template images, and conduct text inpainting to reconstruct incomplete images with text. The code, model, and dataset will be available at https://aka.ms/textdiffuser. |
2023-05-19T00:00:00 | 2305.11173 | Going Denser with Open-Vocabulary Part Segmentation | [
"Peize Sun",
"Shoufa Chen",
"Chenchen Zhu",
"Fanyi Xiao",
"Ping Luo",
"Saining Xie",
"Zhicheng Yan"
] | Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-grained object descriptions, object parts. In this paper, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation. This ability comes from two designs. First, we train the detector on the joint of part-level, object-level and image-level data to build the multi-granularity alignment between language and image. Second, we parse the novel object into its parts by its dense semantic correspondence with the base object. These two designs enable the detector to largely benefit from various data sources and foundation models. In open-vocabulary part segmentation experiments, our method outperforms the baseline by 3.3sim7.3 mAP in cross-dataset generalization on PartImageNet, and improves the baseline by 7.3 novel AP_{50} in cross-category generalization on Pascal Part. Finally, we train a detector that generalizes to a wide range of part segmentation datasets while achieving better performance than dataset-specific training. |
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2023-05-19T00:00:00 | 2305.11000 | SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities | [
"Dong Zhang",
"Shimin Li",
"Xin Zhang",
"Jun Zhan",
"Pengyu Wang",
"Yaqian Zhou",
"Xipeng Qiu"
] | Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT. However, current speech-language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer. In this paper, we propose SpeechGPT, a large language model with intrinsic cross-modal conversational abilities, capable of perceiving and generating multi-model content. With discrete speech representations, we first construct SpeechInstruct, a large-scale cross-modal speech instruction dataset. Additionally, we employ a three-stage training strategy that includes modality-adaptation pre-training, cross-modal instruction fine-tuning, and chain-of-modality instruction fine-tuning. The experimental results demonstrate that SpeechGPT has an impressive capacity to follow multi-modal human instructions and highlight the potential of handling multiple modalities with one model. Demos are shown in https://0nutation.github.io/SpeechGPT.github.io/. |
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2023-05-19T00:00:00 | 2305.11129 | mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences | [
"David Uthus",
"Santiago Ontañón",
"Joshua Ainslie",
"Mandy Guo"
] | We present our work on developing a multilingual, efficient text-to-text transformer that is suitable for handling long inputs. This model, called mLongT5, builds upon the architecture of LongT5, while leveraging the multilingual datasets used for pretraining mT5 and the pretraining tasks of UL2. We evaluate this model on a variety of multilingual summarization and question-answering tasks, and the results show stronger performance for mLongT5 when compared to existing multilingual models such as mBART or M-BERT. |
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2023-05-19T00:00:00 | 2305.10912 | A Generalist Dynamics Model for Control | [
"Ingmar Schubert",
"Jingwei Zhang",
"Jake Bruce",
"Sarah Bechtle",
"Emilio Parisotto",
"Martin Riedmiller",
"Jost Tobias Springenberg",
"Arunkumar Byravan",
"Leonard Hasenclever",
"Nicolas Heess"
] | We investigate the use of transformer sequence models as dynamics models (TDMs) for control. In a number of experiments in the DeepMind control suite, we find that first, TDMs perform well in a single-environment learning setting when compared to baseline models. Second, TDMs exhibit strong generalization capabilities to unseen environments, both in a few-shot setting, where a generalist model is fine-tuned with small amounts of data from the target environment, and in a zero-shot setting, where a generalist model is applied to an unseen environment without any further training. We further demonstrate that generalizing system dynamics can work much better than generalizing optimal behavior directly as a policy. This makes TDMs a promising ingredient for a foundation model of control. |
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2023-05-19T00:00:00 | 2305.11175 | VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks | [
"Wenhai Wang",
"Zhe Chen",
"Xiaokang Chen",
"Jiannan Wu",
"Xizhou Zhu",
"Gang Zeng",
"Ping Luo",
"Tong Lu",
"Jie Zhou",
"Yu Qiao",
"Jifeng Dai"
] | Large language models (LLMs) have notably accelerated progress towards artificial general intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endowing them with immense potential across a range of applications. However, in the field of computer vision, despite the availability of numerous powerful vision foundation models (VFMs), they are still restricted to tasks in a pre-defined form, struggling to match the open-ended task capabilities of LLMs. In this work, we present an LLM-based framework for vision-centric tasks, termed VisionLLM. This framework provides a unified perspective for vision and language tasks by treating images as a foreign language and aligning vision-centric tasks with language tasks that can be flexibly defined and managed using language instructions. An LLM-based decoder can then make appropriate predictions based on these instructions for open-ended tasks. Extensive experiments show that the proposed VisionLLM can achieve different levels of task customization through language instructions, from fine-grained object-level to coarse-grained task-level customization, all with good results. It's noteworthy that, with a generalist LLM-based framework, our model can achieve over 60\% mAP on COCO, on par with detection-specific models. We hope this model can set a new baseline for generalist vision and language models. The demo shall be released based on https://github.com/OpenGVLab/InternGPT. The code shall be released at https://github.com/OpenGVLab/VisionLLM. |
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2023-05-19T00:00:00 | 2305.10688 | MolXPT: Wrapping Molecules with Text for Generative Pre-training | [
"Zequn Liu",
"Wei Zhang",
"Yingce Xia",
"Lijun Wu",
"Shufang Xie",
"Tao Qin",
"Ming Zhang",
"Tie-Yan Liu"
] | Generative pre-trained Transformer (GPT) has demonstrates its great success in natural language processing and related techniques have been adapted into molecular modeling. Considering that text is the most important record for scientific discovery, in this paper, we propose MolXPT, a unified language model of text and molecules pre-trained on SMILES (a sequence representation of molecules) wrapped by text. Briefly, we detect the molecule names in each sequence and replace them to the corresponding SMILES. In this way, the SMILES could leverage the information from surrounding text, and vice versa. The above wrapped sequences, text sequences from PubMed and SMILES sequences from PubChem are all fed into a language model for pre-training. Experimental results demonstrate that MolXPT outperforms strong baselines of molecular property prediction on MoleculeNet, performs comparably to the best model in text-molecule translation while using less than half of its parameters, and enables zero-shot molecular generation without finetuning. |
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2023-05-19T00:00:00 | 2305.11147 | UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild | [
"Can Qin",
"Shu Zhang",
"Ning Yu",
"Yihao Feng",
"Xinyi Yang",
"Yingbo Zhou",
"Huan Wang",
"Juan Carlos Niebles",
"Caiming Xiong",
"Silvio Savarese",
"Stefano Ermon",
"Yun Fu",
"Ran Xu"
] | Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems. Visual generative foundation models such as Stable Diffusion show promise in navigating these goals, especially when prompted with arbitrary languages. However, they often fall short in generating images with spatial, structural, or geometric controls. The integration of such controls, which can accommodate various visual conditions in a single unified model, remains an unaddressed challenge. In response, we introduce UniControl, a new generative foundation model that consolidates a wide array of controllable condition-to-image (C2I) tasks within a singular framework, while still allowing for arbitrary language prompts. UniControl enables pixel-level-precise image generation, where visual conditions primarily influence the generated structures and language prompts guide the style and context. To equip UniControl with the capacity to handle diverse visual conditions, we augment pretrained text-to-image diffusion models and introduce a task-aware HyperNet to modulate the diffusion models, enabling the adaptation to different C2I tasks simultaneously. Trained on nine unique C2I tasks, UniControl demonstrates impressive zero-shot generation abilities with unseen visual conditions. Experimental results show that UniControl often surpasses the performance of single-task-controlled methods of comparable model sizes. This control versatility positions UniControl as a significant advancement in the realm of controllable visual generation. |
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2023-05-19T00:00:00 | 2305.10874 | VideoFactory: Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation | [
"Wenjing Wang",
"Huan Yang",
"Zixi Tuo",
"Huiguo He",
"Junchen Zhu",
"Jianlong Fu",
"Jiaying Liu"
] | We present VideoFactory, an innovative framework for generating high-quality open-domain videos. VideoFactory excels in producing high-definition (1376x768), widescreen (16:9) videos without watermarks, creating an engaging user experience. Generating videos guided by text instructions poses significant challenges, such as modeling the complex relationship between space and time, and the lack of large-scale text-video paired data. Previous approaches extend pretrained text-to-image generation models by adding temporal 1D convolution/attention modules for video generation. However, these approaches overlook the importance of jointly modeling space and time, inevitably leading to temporal distortions and misalignment between texts and videos. In this paper, we propose a novel approach that strengthens the interaction between spatial and temporal perceptions. In particular, we utilize a swapped cross-attention mechanism in 3D windows that alternates the "query" role between spatial and temporal blocks, enabling mutual reinforcement for each other. To fully unlock model capabilities for high-quality video generation, we curate a large-scale video dataset called HD-VG-130M. This dataset comprises 130 million text-video pairs from the open-domain, ensuring high-definition, widescreen and watermark-free characters. Objective metrics and user studies demonstrate the superiority of our approach in terms of per-frame quality, temporal correlation, and text-video alignment, with clear margins. |
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2023-05-19T00:00:00 | 2305.10853 | LDM3D: Latent Diffusion Model for 3D | [
"Gabriela Ben Melech Stan",
"Diana Wofk",
"Scottie Fox",
"Alex Redden",
"Will Saxton",
"Jean Yu",
"Estelle Aflalo",
"Shao-Yen Tseng",
"Fabio Nonato",
"Matthias Muller",
"Vasudev Lal"
] | This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop an application called DepthFusion, which uses the generated RGB images and depth maps to create immersive and interactive 360-degree-view experiences using TouchDesigner. This technology has the potential to transform a wide range of industries, from entertainment and gaming to architecture and design. Overall, this paper presents a significant contribution to the field of generative AI and computer vision, and showcases the potential of LDM3D and DepthFusion to revolutionize content creation and digital experiences. A short video summarizing the approach can be found at https://t.ly/tdi2. |
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2023-05-19T00:00:00 | 2305.10841 | GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework | [
"Ang Lv",
"Xu Tan",
"Peiling Lu",
"Wei Ye",
"Shikun Zhang",
"Jiang Bian",
"Rui Yan"
] | Symbolic music generation aims to create musical notes, which can help users compose music, such as generating target instrumental tracks from scratch, or based on user-provided source tracks. Considering the diverse and flexible combination between source and target tracks, a unified model capable of generating any arbitrary tracks is of crucial necessity. Previous works fail to address this need due to inherent constraints in music representations and model architectures. To address this need, we propose a unified representation and diffusion framework named GETMusic (`GET' stands for GEnerate music Tracks), which includes a novel music representation named GETScore, and a diffusion model named GETDiff. GETScore represents notes as tokens and organizes them in a 2D structure, with tracks stacked vertically and progressing horizontally over time. During training, tracks are randomly selected as either the target or source. In the forward process, target tracks are corrupted by masking their tokens, while source tracks remain as ground truth. In the denoising process, GETDiff learns to predict the masked target tokens, conditioning on the source tracks. With separate tracks in GETScore and the non-autoregressive behavior of the model, GETMusic can explicitly control the generation of any target tracks from scratch or conditioning on source tracks. We conduct experiments on music generation involving six instrumental tracks, resulting in a total of 665 combinations. GETMusic provides high-quality results across diverse combinations and surpasses prior works proposed for some specific combinations. |
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2023-05-19T00:00:00 | 2305.10722 | Discriminative Diffusion Models as Few-shot Vision and Language Learners | [
"Xuehai He",
"Weixi Feng",
"Tsu-Jui Fu",
"Varun Jampani",
"Arjun Akula",
"Pradyumna Narayana",
"Sugato Basu",
"William Yang Wang",
"Xin Eric Wang"
] | Diffusion models, such as Stable Diffusion, have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trained diffusion models for discriminative tasks such as image-text matching? To answer this question, we propose a novel approach, Discriminative Stable Diffusion (DSD), which turns pre-trained text-to-image diffusion models into few-shot discriminative learners. Our approach uses the cross-attention score of a Stable Diffusion model to capture the mutual influence between visual and textual information and fine-tune the model via attention-based prompt learning to perform image-text matching. By comparing DSD with state-of-the-art methods on several benchmark datasets, we demonstrate the potential of using pre-trained diffusion models for discriminative tasks with superior results on few-shot image-text matching. |
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2023-05-19T00:00:00 | 2305.10474 | Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models | [
"Songwei Ge",
"Seungjun Nah",
"Guilin Liu",
"Tyler Poon",
"Andrew Tao",
"Bryan Catanzaro",
"David Jacobs",
"Jia-Bin Huang",
"Ming-Yu Liu",
"Yogesh Balaji"
] | Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy. While off-the-shelf billion-scale datasets for image generation are available, collecting similar video data of the same scale is still challenging. Also, training a video diffusion model is computationally much more expensive than its image counterpart. In this work, we explore finetuning a pretrained image diffusion model with video data as a practical solution for the video synthesis task. We find that naively extending the image noise prior to video noise prior in video diffusion leads to sub-optimal performance. Our carefully designed video noise prior leads to substantially better performance. Extensive experimental validation shows that our model, Preserve Your Own Correlation (PYoCo), attains SOTA zero-shot text-to-video results on the UCF-101 and MSR-VTT benchmarks. It also achieves SOTA video generation quality on the small-scale UCF-101 benchmark with a 10times smaller model using significantly less computation than the prior art. |
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2023-05-22T00:00:00 | 2305.11206 | LIMA: Less Is More for Alignment | [
"Chunting Zhou",
"Pengfei Liu",
"Puxin Xu",
"Srini Iyer",
"Jiao Sun",
"Yuning Mao",
"Xuezhe Ma",
"Avia Efrat",
"Ping Yu",
"Lili Yu",
"Susan Zhang",
"Gargi Ghosh",
"Mike Lewis",
"Luke Zettlemoyer",
"Omer Levy"
] | Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65B parameter LLaMa language model fine-tuned with the standard supervised loss on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling. LIMA demonstrates remarkably strong performance, learning to follow specific response formats from only a handful of examples in the training data, including complex queries that range from planning trip itineraries to speculating about alternate history. Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data. In a controlled human study, responses from LIMA are either equivalent or strictly preferred to GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard and 65% versus DaVinci003, which was trained with human feedback. Taken together, these results strongly suggest that almost all knowledge in large language models is learned during pretraining, and only limited instruction tuning data is necessary to teach models to produce high quality output. |
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2023-05-22T00:00:00 | 2305.11870 | Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic Models | [
"Byungjun Kim",
"Patrick Kwon",
"Kwangho Lee",
"Myunggi Lee",
"Sookwan Han",
"Daesik Kim",
"Hanbyul Joo"
] | We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic details, the generation of 3D human meshes has been a challenging problem. To address this, we decompose the problem into 2D normal map generation and normal map-based 3D reconstruction. Specifically, we first simultaneously generate realistic normal maps for the front and backside of a clothed human, dubbed dual normal maps, using a pose-conditional diffusion model. For 3D reconstruction, we ``carve'' the prior SMPL-X mesh to a detailed 3D mesh according to the normal maps through mesh optimization. To further enhance the high-frequency details, we present a diffusion resampling scheme on both body and facial regions, thus encouraging the generation of realistic digital avatars. We also seamlessly incorporate a recent text-to-image diffusion model to support text-based human identity control. Our method, namely, Chupa, is capable of generating realistic 3D clothed humans with better perceptual quality and identity variety. |
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2023-05-22T00:00:00 | 2305.11854 | Multimodal Web Navigation with Instruction-Finetuned Foundation Models | [
"Hiroki Furuta",
"Ofir Nachum",
"Kuang-Huei Lee",
"Yutaka Matsuo",
"Shixiang Shane Gu",
"Izzeddin Gur"
] | The progress of autonomous web navigation has been hindered by the dependence on billions of exploratory interactions via online reinforcement learning, and domain-specific model designs that make it difficult to leverage generalization from rich out-of-domain data. In this work, we study data-driven offline training for web agents with vision-language foundation models. We propose an instruction-following multimodal agent, WebGUM, that observes both webpage screenshots and HTML pages and outputs web navigation actions, such as click and type. WebGUM is trained by jointly finetuning an instruction-finetuned language model and a vision transformer on a large corpus of demonstrations. We empirically demonstrate this recipe improves the agent's ability of grounded visual perception, HTML comprehension and multi-step reasoning, outperforming prior works by a significant margin. On the MiniWoB benchmark, we improve over the previous best offline methods by more than 31.9%, being close to reaching online-finetuned SoTA. On the WebShop benchmark, our 3-billion-parameter model achieves superior performance to the existing SoTA, PaLM-540B. We also collect 347K high-quality demonstrations using our trained models, 38 times larger than prior work, and make them available to promote future research in this direction. |
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2023-05-22T00:00:00 | 2305.11588 | Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields | [
"Jingbo Zhang",
"Xiaoyu Li",
"Ziyu Wan",
"Can Wang",
"Jing Liao"
] | Text-driven 3D scene generation is widely applicable to video gaming, film industry, and metaverse applications that have a large demand for 3D scenes. However, existing text-to-3D generation methods are limited to producing 3D objects with simple geometries and dreamlike styles that lack realism. In this work, we present Text2NeRF, which is able to generate a wide range of 3D scenes with complicated geometric structures and high-fidelity textures purely from a text prompt. To this end, we adopt NeRF as the 3D representation and leverage a pre-trained text-to-image diffusion model to constrain the 3D reconstruction of the NeRF to reflect the scene description. Specifically, we employ the diffusion model to infer the text-related image as the content prior and use a monocular depth estimation method to offer the geometric prior. Both content and geometric priors are utilized to update the NeRF model. To guarantee textured and geometric consistency between different views, we introduce a progressive scene inpainting and updating strategy for novel view synthesis of the scene. Our method requires no additional training data but only a natural language description of the scene as the input. Extensive experiments demonstrate that our Text2NeRF outperforms existing methods in producing photo-realistic, multi-view consistent, and diverse 3D scenes from a variety of natural language prompts. |
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2023-05-22T00:00:00 | 2305.11694 | QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations | [
"Chaitanya Malaviya",
"Peter Shaw",
"Ming-Wei Chang",
"Kenton Lee",
"Kristina Toutanova"
] | Formulating selective information needs results in queries that implicitly specify set operations, such as intersection, union, and difference. For instance, one might search for "shorebirds that are not sandpipers" or "science-fiction films shot in England". To study the ability of retrieval systems to meet such information needs, we construct QUEST, a dataset of 3357 natural language queries with implicit set operations, that map to a set of entities corresponding to Wikipedia documents. The dataset challenges models to match multiple constraints mentioned in queries with corresponding evidence in documents and correctly perform various set operations. The dataset is constructed semi-automatically using Wikipedia category names. Queries are automatically composed from individual categories, then paraphrased and further validated for naturalness and fluency by crowdworkers. Crowdworkers also assess the relevance of entities based on their documents and highlight attribution of query constraints to spans of document text. We analyze several modern retrieval systems, finding that they often struggle on such queries. Queries involving negation and conjunction are particularly challenging and systems are further challenged with combinations of these operations. |
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2023-05-22T00:00:00 | 2305.11841 | How Does Generative Retrieval Scale to Millions of Passages? | [
"Ronak Pradeep",
"Kai Hui",
"Jai Gupta",
"Adam D. Lelkes",
"Honglei Zhuang",
"Jimmy Lin",
"Donald Metzler",
"Vinh Q. Tran"
] | Popularized by the Differentiable Search Index, the emerging paradigm of generative retrieval re-frames the classic information retrieval problem into a sequence-to-sequence modeling task, forgoing external indices and encoding an entire document corpus within a single Transformer. Although many different approaches have been proposed to improve the effectiveness of generative retrieval, they have only been evaluated on document corpora on the order of 100k in size. We conduct the first empirical study of generative retrieval techniques across various corpus scales, ultimately scaling up to the entire MS MARCO passage ranking task with a corpus of 8.8M passages and evaluating model sizes up to 11B parameters. We uncover several findings about scaling generative retrieval to millions of passages; notably, the central importance of using synthetic queries as document representations during indexing, the ineffectiveness of existing proposed architecture modifications when accounting for compute cost, and the limits of naively scaling model parameters with respect to retrieval performance. While we find that generative retrieval is competitive with state-of-the-art dual encoders on small corpora, scaling to millions of passages remains an important and unsolved challenge. We believe these findings will be valuable for the community to clarify the current state of generative retrieval, highlight the unique challenges, and inspire new research directions. |
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2023-05-22T00:00:00 | 2305.11834 | Pengi: An Audio Language Model for Audio Tasks | [
"Soham Deshmukh",
"Benjamin Elizalde",
"Rita Singh",
"Huaming Wang"
] | In the domain of audio processing, Transfer Learning has facilitated the rise of Self-Supervised Learning and Zero-Shot Learning techniques. These approaches have led to the development of versatile models capable of tackling a wide array of tasks, while delivering state-of-the-art performance. However, current models inherently lack the capacity to produce the requisite language for open-ended tasks, such as Audio Captioning or Audio Question & Answering. We introduce Pengi, a novel Audio Language Model that leverages Transfer Learning by framing all audio tasks as text-generation tasks. It takes as input, an audio recording, and text, and generates free-form text as output. The input audio is represented as a sequence of continuous embeddings by an audio encoder. A text encoder does the same for the corresponding text input. Both sequences are combined as a prefix to prompt a pre-trained frozen language model. The unified architecture of Pengi enables open-ended tasks and close-ended tasks without any additional fine-tuning or task-specific extensions. When evaluated on 22 downstream tasks, our approach yields state-of-the-art performance in several of them. Our results show that connecting language models with audio models is a major step towards general-purpose audio understanding |
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2023-05-22T00:00:00 | 2305.11863 | Scaling laws for language encoding models in fMRI | [
"Richard Antonello",
"Aditya Vaidya",
"Alexander G. Huth"
] | Representations from transformer-based unidirectional language models are known to be effective at predicting brain responses to natural language. However, most studies comparing language models to brains have used GPT-2 or similarly sized language models. Here we tested whether larger open-source models such as those from the OPT and LLaMA families are better at predicting brain responses recorded using fMRI. Mirroring scaling results from other contexts, we found that brain prediction performance scales log-linearly with model size from 125M to 30B parameter models, with ~15% increased encoding performance as measured by correlation with a held-out test set across 3 subjects. Similar log-linear behavior was observed when scaling the size of the fMRI training set. We also characterized scaling for acoustic encoding models that use HuBERT, WavLM, and Whisper, and we found comparable improvements with model size. A noise ceiling analysis of these large, high-performance encoding models showed that performance is nearing the theoretical maximum for brain areas such as the precuneus and higher auditory cortex. These results suggest that increasing scale in both models and data will yield incredibly effective models of language processing in the brain, enabling better scientific understanding as well as applications such as decoding. |
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2023-05-22T00:00:00 | 2305.11840 | SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models | [
"Akshita Jha",
"Aida Davani",
"Chandan K. Reddy",
"Shachi Dave",
"Vinodkumar Prabhakaran",
"Sunipa Dev"
] | Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models. However, existing datasets are limited in size and coverage, and are largely restricted to stereotypes prevalent in the Western society. This is especially problematic as language technologies gain hold across the globe. To address this gap, we present SeeGULL, a broad-coverage stereotype dataset, built by utilizing generative capabilities of large language models such as PaLM, and GPT-3, and leveraging a globally diverse rater pool to validate the prevalence of those stereotypes in society. SeeGULL is in English, and contains stereotypes about identity groups spanning 178 countries across 8 different geo-political regions across 6 continents, as well as state-level identities within the US and India. We also include fine-grained offensiveness scores for different stereotypes and demonstrate their global disparities. Furthermore, we include comparative annotations about the same groups by annotators living in the region vs. those that are based in North America, and demonstrate that within-region stereotypes about groups differ from those prevalent in North America. CONTENT WARNING: This paper contains stereotype examples that may be offensive. |
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2023-05-22T00:00:00 | 2305.11778 | Cross-Lingual Supervision improves Large Language Models Pre-training | [
"Andrea Schioppa",
"Xavier Garcia",
"Orhan Firat"
] | The recent rapid progress in pre-training Large Language Models has relied on using self-supervised language modeling objectives like next token prediction or span corruption. On the other hand, Machine Translation Systems are mostly trained using cross-lingual supervision that requires aligned data between source and target languages. We demonstrate that pre-training Large Language Models on a mixture of a self-supervised Language Modeling objective and the supervised Machine Translation objective, therefore including cross-lingual parallel data during pre-training, yields models with better in-context learning abilities. As pre-training is a very resource-intensive process and a grid search on the best mixing ratio between the two objectives is prohibitively expensive, we propose a simple yet effective strategy to learn it during pre-training. |
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2023-05-22T00:00:00 | 2305.11759 | Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning | [
"Mustafa Safa Ozdayi",
"Charith Peris",
"Jack FitzGerald",
"Christophe Dupuy",
"Jimit Majmudar",
"Haidar Khan",
"Rahil Parikh",
"Rahul Gupta"
] | Large Language Models (LLMs) are known to memorize significant portions of their training data. Parts of this memorized content have been shown to be extractable by simply querying the model, which poses a privacy risk. We present a novel approach which uses prompt-tuning to control the extraction rates of memorized content in LLMs. We present two prompt training strategies to increase and decrease extraction rates, which correspond to an attack and a defense, respectively. We demonstrate the effectiveness of our techniques by using models from the GPT-Neo family on a public benchmark. For the 1.3B parameter GPT-Neo model, our attack yields a 9.3 percentage point increase in extraction rate compared to our baseline. Our defense can be tuned to achieve different privacy-utility trade-offs by a user-specified hyperparameter. We achieve an extraction rate reduction of up to 97.7% relative to our baseline, with a perplexity increase of 16.9%. |
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2023-05-22T00:00:00 | 2305.11738 | CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing | [
"Zhibin Gou",
"Zhihong Shao",
"Yeyun Gong",
"Yelong Shen",
"Yujiu Yang",
"Nan Duan",
"Weizhu Chen"
] | Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating offensive and toxic content. Unlike these models, humans typically utilize external tools to cross-check and refine their initial content, like using a search engine for fact-checking, or a code interpreter for debugging. Inspired by this observation, we introduce a framework called CRITIC that allows LLMs, which are essentially "black boxes" to validate and progressively amend their own outputs in a manner similar to human interaction with tools. More specifically, starting with an initial output, CRITIC interacts with appropriate tools to evaluate certain aspects of the text, and then revises the output based on the feedback obtained during this validation process. Comprehensive evaluations involving free-form question answering, mathematical program synthesis, and toxicity reduction demonstrate that CRITIC consistently enhances the performance of LLMs. Meanwhile, our research highlights the crucial importance of external feedback in promoting the ongoing self-improvement of LLMs. |
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2023-05-22T00:00:00 | 2305.11598 | Introspective Tips: Large Language Model for In-Context Decision Making | [
"Liting Chen",
"Lu Wang",
"Hang Dong",
"Yali Du",
"Jie Yan",
"Fangkai Yang",
"Shuang Li",
"Pu Zhao",
"Si Qin",
"Saravan Rajmohan",
"Qingwei Lin",
"Dongmei Zhang"
] | The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks. In this study, we employ ``Introspective Tips" to facilitate LLMs in self-optimizing their decision-making. By introspectively examining trajectories, LLM refines its policy by generating succinct and valuable tips. Our method enhances the agent's performance in both few-shot and zero-shot learning situations by considering three essential scenarios: learning from the agent's past experiences, integrating expert demonstrations, and generalizing across diverse games. Importantly, we accomplish these improvements without fine-tuning the LLM parameters; rather, we adjust the prompt to generalize insights from the three aforementioned situations. Our framework not only supports but also emphasizes the advantage of employing LLM in in-contxt decision-making. Experiments involving over 100 games in TextWorld illustrate the superior performance of our approach. |
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2023-05-22T00:00:00 | 2305.11541 | Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering | [
"Zezhong Wang",
"Fangkai Yang",
"Pu Zhao",
"Lu Wang",
"Jue Zhang",
"Mohit Garg",
"Qingwei Lin",
"Dongmei Zhang"
] | Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average since there is no specific knowledge in it. This issue has attracted widespread attention, but there are few relevant benchmarks available. In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, which is about Microsoft products and IT technical problems encountered by customers. This dataset contains industry cloud-specific QA knowledge, which is not available for general LLM, so it is well suited for evaluating methods aimed at improving domain-specific capabilities of LLM. In addition, we propose a new model interaction paradigm that can empower LLM to achieve better performance on domain-specific tasks where it is not proficient. Extensive experiments demonstrate that the approach following our model fusion framework outperforms the commonly used LLM with retrieval methods. |
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2023-05-22T00:00:00 | 2305.11364 | Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models | [
"Emily Reif",
"Minsuk Kahng",
"Savvas Petridis"
] | Large language models (LLMs) can be used to generate smaller, more refined datasets via few-shot prompting for benchmarking, fine-tuning or other use cases. However, understanding and evaluating these datasets is difficult, and the failure modes of LLM-generated data are still not well understood. Specifically, the data can be repetitive in surprising ways, not only semantically but also syntactically and lexically. We present LinguisticLens, a novel inter-active visualization tool for making sense of and analyzing syntactic diversity of LLM-generated datasets. LinguisticLens clusters text along syntactic, lexical, and semantic axes. It supports hierarchical visualization of a text dataset, allowing users to quickly scan for an overview and inspect individual examples. The live demo is available at shorturl.at/zHOUV. |
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2023-05-22T00:00:00 | 2305.11308 | Counterfactuals for Design: A Model-Agnostic Method For Design Recommendations | [
"Lyle Regenwetter",
"Yazan Abu Obaideh",
"Faez Ahmed"
] | We introduce Multi-Objective Counterfactuals for Design (MCD), a novel method for counterfactual optimization in design problems. Counterfactuals are hypothetical situations that can lead to a different decision or choice. In this paper, the authors frame the counterfactual search problem as a design recommendation tool that can help identify modifications to a design, leading to better functional performance. MCD improves upon existing counterfactual search methods by supporting multi-objective queries, which are crucial in design problems, and by decoupling the counterfactual search and sampling processes, thus enhancing efficiency and facilitating objective tradeoff visualization. The paper demonstrates MCD's core functionality using a two-dimensional test case, followed by three case studies of bicycle design that showcase MCD's effectiveness in real-world design problems. In the first case study, MCD excels at recommending modifications to query designs that can significantly enhance functional performance, such as weight savings and improvements to the structural safety factor. The second case study demonstrates that MCD can work with a pre-trained language model to suggest design changes based on a subjective text prompt effectively. Lastly, the authors task MCD with increasing a query design's similarity to a target image and text prompt while simultaneously reducing weight and improving structural performance, demonstrating MCD's performance on a complex multimodal query. Overall, MCD has the potential to provide valuable recommendations for practitioners and design automation researchers looking for answers to their ``What if'' questions by exploring hypothetical design modifications and their impact on multiple design objectives. The code, test problems, and datasets used in the paper are available to the public at decode.mit.edu/projects/counterfactuals/. |
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2023-05-22T00:00:00 | 2305.11243 | Comparing Machines and Children: Using Developmental Psychology Experiments to Assess the Strengths and Weaknesses of LaMDA Responses | [
"Eliza Kosoy",
"Emily Rose Reagan",
"Leslie Lai",
"Alison Gopnik",
"Danielle Krettek Cobb"
] | Developmental psychologists have spent decades devising experiments to test the intelligence and knowledge of infants and children, tracing the origin of crucial concepts and capacities. Moreover, experimental techniques in developmental psychology have been carefully designed to discriminate the cognitive capacities that underlie particular behaviors. We propose that using classical experiments from child development is a particularly effective way to probe the computational abilities of AI models, in general, and LLMs in particular. First, the methodological techniques of developmental psychology, such as the use of novel stimuli to control for past experience or control conditions to determine whether children are using simple associations, can be equally helpful for assessing the capacities of LLMs. In parallel, testing LLMs in this way can tell us whether the information that is encoded in text is sufficient to enable particular responses, or whether those responses depend on other kinds of information, such as information from exploration of the physical world. In this work we adapt classical developmental experiments to evaluate the capabilities of LaMDA, a large language model from Google. We propose a novel LLM Response Score (LRS) metric which can be used to evaluate other language models, such as GPT. We find that LaMDA generates appropriate responses that are similar to those of children in experiments involving social understanding, perhaps providing evidence that knowledge of these domains is discovered through language. On the other hand, LaMDA's responses in early object and action understanding, theory of mind, and especially causal reasoning tasks are very different from those of young children, perhaps showing that these domains require more real-world, self-initiated exploration and cannot simply be learned from patterns in language input. |
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2023-05-22T00:00:00 | 2305.11837 | Comparing Software Developers with ChatGPT: An Empirical Investigation | [
"Nathalia Nascimento",
"Paulo Alencar",
"Donald Cowan"
] | The advent of automation in particular Software Engineering (SE) tasks has transitioned from theory to reality. Numerous scholarly articles have documented the successful application of Artificial Intelligence to address issues in areas such as project management, modeling, testing, and development. A recent innovation is the introduction of ChatGPT, an ML-infused chatbot, touted as a resource proficient in generating programming codes and formulating software testing strategies for developers and testers respectively. Although there is speculation that AI-based computation can increase productivity and even substitute software engineers in software development, there is currently a lack of empirical evidence to verify this. Moreover, despite the primary focus on enhancing the accuracy of AI systems, non-functional requirements including energy efficiency, vulnerability, fairness (i.e., human bias), and safety frequently receive insufficient attention. This paper posits that a comprehensive comparison of software engineers and AI-based solutions, considering various evaluation criteria, is pivotal in fostering human-machine collaboration, enhancing the reliability of AI-based methods, and understanding task suitability for humans or AI. Furthermore, it facilitates the effective implementation of cooperative work structures and human-in-the-loop processes. This paper conducts an empirical investigation, contrasting the performance of software engineers and AI systems, like ChatGPT, across different evaluation metrics. The empirical study includes a case of assessing ChatGPT-generated code versus code produced by developers and uploaded in Leetcode. |
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2023-05-22T00:00:00 | 2305.11846 | Any-to-Any Generation via Composable Diffusion | [
"Zineng Tang",
"Ziyi Yang",
"Chenguang Zhu",
"Michael Zeng",
"Mohit Bansal"
] | We present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities. Unlike existing generative AI systems, CoDi can generate multiple modalities in parallel and its input is not limited to a subset of modalities like text or image. Despite the absence of training datasets for many combinations of modalities, we propose to align modalities in both the input and output space. This allows CoDi to freely condition on any input combination and generate any group of modalities, even if they are not present in the training data. CoDi employs a novel composable generation strategy which involves building a shared multimodal space by bridging alignment in the diffusion process, enabling the synchronized generation of intertwined modalities, such as temporally aligned video and audio. Highly customizable and flexible, CoDi achieves strong joint-modality generation quality, and outperforms or is on par with the unimodal state-of-the-art for single-modality synthesis. The project page with demonstrations and code is at https://codi-gen.github.io |
|
2023-05-22T00:00:00 | 2305.11675 | Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity | [
"Zijiao Chen",
"Jiaxin Qing",
"Juan Helen Zhou"
] | Reconstructing human vision from brain activities has been an appealing task that helps to understand our cognitive process. Even though recent research has seen great success in reconstructing static images from non-invasive brain recordings, work on recovering continuous visual experiences in the form of videos is limited. In this work, we propose Mind-Video that learns spatiotemporal information from continuous fMRI data of the cerebral cortex progressively through masked brain modeling, multimodal contrastive learning with spatiotemporal attention, and co-training with an augmented Stable Diffusion model that incorporates network temporal inflation. We show that high-quality videos of arbitrary frame rates can be reconstructed with Mind-Video using adversarial guidance. The recovered videos were evaluated with various semantic and pixel-level metrics. We achieved an average accuracy of 85% in semantic classification tasks and 0.19 in structural similarity index (SSIM), outperforming the previous state-of-the-art by 45%. We also show that our model is biologically plausible and interpretable, reflecting established physiological processes. |
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2023-05-22T00:00:00 | 2305.11337 | RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture | [
"Liangchen Song",
"Liangliang Cao",
"Hongyu Xu",
"Kai Kang",
"Feng Tang",
"Junsong Yuan",
"Yang Zhao"
] | The techniques for 3D indoor scene capturing are widely used, but the meshes produced leave much to be desired. In this paper, we propose "RoomDreamer", which leverages powerful natural language to synthesize a new room with a different style. Unlike existing image synthesis methods, our work addresses the challenge of synthesizing both geometry and texture aligned to the input scene structure and prompt simultaneously. The key insight is that a scene should be treated as a whole, taking into account both scene texture and geometry. The proposed framework consists of two significant components: Geometry Guided Diffusion and Mesh Optimization. Geometry Guided Diffusion for 3D Scene guarantees the consistency of the scene style by applying the 2D prior to the entire scene simultaneously. Mesh Optimization improves the geometry and texture jointly and eliminates the artifacts in the scanned scene. To validate the proposed method, real indoor scenes scanned with smartphones are used for extensive experiments, through which the effectiveness of our method is demonstrated. |
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2023-05-23T00:00:00 | 2305.13077 | ControlVideo: Training-free Controllable Text-to-Video Generation | [
"Yabo Zhang",
"Yuxiang Wei",
"Dongsheng Jiang",
"Xiaopeng Zhang",
"Wangmeng Zuo",
"Qi Tian"
] | https://github.com/YBYBZhang/ControlVideo | Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling. Besides the training burden, the generated videos also suffer from appearance inconsistency and structural flickers, especially in long video synthesis. To address these challenges, we design a training-free framework called ControlVideo to enable natural and efficient text-to-video generation. ControlVideo, adapted from ControlNet, leverages coarsely structural consistency from input motion sequences, and introduces three modules to improve video generation. Firstly, to ensure appearance coherence between frames, ControlVideo adds fully cross-frame interaction in self-attention modules. Secondly, to mitigate the flicker effect, it introduces an interleaved-frame smoother that employs frame interpolation on alternated frames. Finally, to produce long videos efficiently, it utilizes a hierarchical sampler that separately synthesizes each short clip with holistic coherency. Empowered with these modules, ControlVideo outperforms the state-of-the-arts on extensive motion-prompt pairs quantitatively and qualitatively. Notably, thanks to the efficient designs, it generates both short and long videos within several minutes using one NVIDIA 2080Ti. Code is available at https://github.com/YBYBZhang/ControlVideo. |
2023-05-23T00:00:00 | 2305.13301 | Training Diffusion Models with Reinforcement Learning | [
"Kevin Black",
"Michael Janner",
"Yilun Du",
"Ilya Kostrikov",
"Sergey Levine"
] | Diffusion models are a class of flexible generative models trained with an approximation to the log-likelihood objective. However, most use cases of diffusion models are not concerned with likelihoods, but instead with downstream objectives such as human-perceived image quality or drug effectiveness. In this paper, we investigate reinforcement learning methods for directly optimizing diffusion models for such objectives. We describe how posing denoising as a multi-step decision-making problem enables a class of policy gradient algorithms, which we refer to as denoising diffusion policy optimization (DDPO), that are more effective than alternative reward-weighted likelihood approaches. Empirically, DDPO is able to adapt text-to-image diffusion models to objectives that are difficult to express via prompting, such as image compressibility, and those derived from human feedback, such as aesthetic quality. Finally, we show that DDPO can improve prompt-image alignment using feedback from a vision-language model without the need for additional data collection or human annotation. |
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2023-05-23T00:00:00 | 2305.13048 | RWKV: Reinventing RNNs for the Transformer Era | [
"Bo Peng",
"Eric Alcaide",
"Quentin Anthony",
"Alon Albalak",
"Samuel Arcadinho",
"Huanqi Cao",
"Xin Cheng",
"Michael Chung",
"Matteo Grella",
"Kranthi Kiran GV",
"Xuzheng He",
"Haowen Hou",
"Przemyslaw Kazienko",
"Jan Kocon",
"Jiaming Kong",
"Bartlomiej Koptyra",
"Hayden Lau",
"Krishna Sri Ipsit Mantri",
"Ferdinand Mom",
"Atsushi Saito",
"Xiangru Tang",
"Bolun Wang",
"Johan S. Wind",
"Stansilaw Wozniak",
"Ruichong Zhang",
"Zhenyuan Zhang",
"Qihang Zhao",
"Peng Zhou",
"Jian Zhu",
"Rui-Jie Zhu"
] | Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of Transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, which parallelizes computations during training and maintains constant computational and memory complexity during inference, leading to the first non-transformer architecture to be scaled to tens of billions of parameters. Our experiments reveal that RWKV performs on par with similarly sized Transformers, suggesting that future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling the trade-offs between computational efficiency and model performance in sequence processing tasks. |
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2023-05-23T00:00:00 | 2305.13050 | AudioToken: Adaptation of Text-Conditioned Diffusion Models for Audio-to-Image Generation | [
"Guy Yariv",
"Itai Gat",
"Lior Wolf",
"Yossi Adi",
"Idan Schwartz"
] | In recent years, image generation has shown a great leap in performance, where diffusion models play a central role. Although generating high-quality images, such models are mainly conditioned on textual descriptions. This begs the question: "how can we adopt such models to be conditioned on other modalities?". In this paper, we propose a novel method utilizing latent diffusion models trained for text-to-image-generation to generate images conditioned on audio recordings. Using a pre-trained audio encoding model, the proposed method encodes audio into a new token, which can be considered as an adaptation layer between the audio and text representations. Such a modeling paradigm requires a small number of trainable parameters, making the proposed approach appealing for lightweight optimization. Results suggest the proposed method is superior to the evaluated baseline methods, considering objective and subjective metrics. Code and samples are available at: https://pages.cs.huji.ac.il/adiyoss-lab/AudioToken. |
|
2023-05-23T00:00:00 | 2305.13009 | Textually Pretrained Speech Language Models | [
"Michael Hassid",
"Tal Remez",
"Tu Anh Nguyen",
"Itai Gat",
"Alexis Conneau",
"Felix Kreuk",
"Jade Copet",
"Alexandre Defossez",
"Gabriel Synnaeve",
"Emmanuel Dupoux",
"Roy Schwartz",
"Yossi Adi"
] | Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different model design choices such as the speech tokenizer, the pretrained textual model, and the dataset size. We find that model and dataset scale both play an important role in constructing better-performing SpeechLMs. Based on our observations, we present the largest (to the best of our knowledge) SpeechLM both in terms of number of parameters and training data. We additionally introduce two spoken versions of the StoryCloze textual benchmark to further improve model evaluation and advance future research in the field. Speech samples can be found on our website: https://pages.cs.huji.ac.il/adiyoss-lab/twist/ . |
|
2023-05-23T00:00:00 | 2305.12487 | Augmenting Autotelic Agents with Large Language Models | [
"Cédric Colas",
"Laetitia Teodorescu",
"Pierre-Yves Oudeyer",
"Xingdi Yuan",
"Marc-Alexandre Côté"
] | Humans learn to master open-ended repertoires of skills by imagining and practicing their own goals. This autotelic learning process, literally the pursuit of self-generated (auto) goals (telos), becomes more and more open-ended as the goals become more diverse, abstract and creative. The resulting exploration of the space of possible skills is supported by an inter-individual exploration: goal representations are culturally evolved and transmitted across individuals, in particular using language. Current artificial agents mostly rely on predefined goal representations corresponding to goal spaces that are either bounded (e.g. list of instructions), or unbounded (e.g. the space of possible visual inputs) but are rarely endowed with the ability to reshape their goal representations, to form new abstractions or to imagine creative goals. In this paper, we introduce a language model augmented autotelic agent (LMA3) that leverages a pretrained language model (LM) to support the representation, generation and learning of diverse, abstract, human-relevant goals. The LM is used as an imperfect model of human cultural transmission; an attempt to capture aspects of humans' common-sense, intuitive physics and overall interests. Specifically, it supports three key components of the autotelic architecture: 1)~a relabeler that describes the goals achieved in the agent's trajectories, 2)~a goal generator that suggests new high-level goals along with their decomposition into subgoals the agent already masters, and 3)~reward functions for each of these goals. Without relying on any hand-coded goal representations, reward functions or curriculum, we show that LMA3 agents learn to master a large diversity of skills in a task-agnostic text-based environment. |
|
2023-05-23T00:00:00 | 2305.12050 | CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code Authoring | [
"Vijayaraghavan Murali",
"Chandra Maddila",
"Imad Ahmad",
"Michael Bolin",
"Daniel Cheng",
"Negar Ghorbani",
"Renuka Fernandez",
"Nachiappan Nagappan"
] | The rise of large language models (LLMs) has unlocked various applications of this technology in software development. In particular, generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we present CodeCompose, an AI-assisted code authoring tool developed and deployed at Meta internally. CodeCompose is based on the InCoder LLM that merges generative capabilities with bi-directionality. We have scaled up CodeCompose to serve tens of thousands of developers at Meta, across 10+ programming languages and several coding surfaces. We discuss unique challenges in terms of user experience and metrics that arise when deploying such tools in large-scale industrial settings. We present our experience in making design decisions about the model and system architecture for CodeCompose that addresses these challenges. Finally, we present metrics from our large-scale deployment of CodeCompose that shows its impact on Meta's internal code authoring experience over a 15-day time window, where 4.5 million suggestions were made by CodeCompose. Quantitative metrics reveal that (i) CodeCompose has an acceptance rate of 22% across several languages, and (ii) 8% of the code typed by users of CodeCompose is through accepting code suggestions from CodeCompose. Qualitative feedback indicates an overwhelming 91.5% positive reception for CodeCompose. In addition to assisting with code authoring, CodeCompose is also introducing other positive side effects such as encouraging developers to generate more in-code documentation, helping them with the discovery of new APIs, etc. |
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2023-05-23T00:00:00 | 2305.12001 | OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models | [
"Badr AlKhamissi",
"Siddharth Verma",
"Ping Yu",
"Zhijing Jin",
"Asli Celikyilmaz",
"Mona Diab"
] | In this paper, we conduct a thorough investigation into the reasoning capabilities of Large Language Models (LLMs), focusing specifically on the Open Pretrained Transformers (OPT) models as a representative of such models. Our study entails finetuning three different sizes of OPT on a carefully curated reasoning corpus, resulting in two sets of finetuned models: OPT-R, finetuned without explanations, and OPT-RE, finetuned with explanations. We then evaluate all models on 57 out-of-domain tasks drawn from the SUPER-NATURALINSTRUCTIONS benchmark, covering 26 distinct reasoning skills, utilizing three prompting techniques. Through a comprehensive grid of 27 configurations and 6,156 test evaluations, we investigate the dimensions of finetuning, prompting, and scale to understand the role of explanations on different reasoning skills. Our findings reveal that having explanations in the fewshot exemplar has no significant impact on the model's performance when the model is finetuned, while positively affecting the non-finetuned counterpart. Moreover, we observe a slight yet consistent increase in classification accuracy as we incorporate explanations during prompting and finetuning, respectively. Finally, we offer insights on which skills benefit the most from incorporating explanations during finetuning and prompting, such as Numerical (+20.4%) and Analogical (+13.9%) reasoning, as well as skills that exhibit negligible or negative effects. |
|
2023-05-23T00:00:00 | 2305.11938 | XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages | [
"Sebastian Ruder",
"Jonathan H. Clark",
"Alexander Gutkin",
"Mihir Kale",
"Min Ma",
"Massimo Nicosia",
"Shruti Rijhwani",
"Parker Riley",
"Jean-Michel A. Sarr",
"Xinyi Wang",
"John Wieting",
"Nitish Gupta",
"Anna Katanova",
"Christo Kirov",
"Dana L. Dickinson",
"Brian Roark",
"Bidisha Samanta",
"Connie Tao",
"David I. Adelani",
"Vera Axelrod",
"Isaac Caswell",
"Colin Cherry",
"Dan Garrette",
"Reeve Ingle",
"Melvin Johnson",
"Dmitry Panteleev",
"Partha Talukdar"
] | Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- languages for which NLP re-search is particularly far behind in meeting user needs -- it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks -- tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text-only, multi-modal (vision, audio, and text),supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models |
|
2023-05-23T00:00:00 | 2305.13304 | RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text | [
"Wangchunshu Zhou",
"Yuchen Eleanor Jiang",
"Peng Cui",
"Tiannan Wang",
"Zhenxin Xiao",
"Yifan Hou",
"Ryan Cotterell",
"Mrinmaya Sachan"
] | https://github.com/aiwaves-cn/RecurrentGPT | The fixed-size context of Transformer makes GPT models incapable of generating arbitrarily long text. In this paper, we introduce RecurrentGPT, a language-based simulacrum of the recurrence mechanism in RNNs. RecurrentGPT is built upon a large language model (LLM) such as ChatGPT and uses natural language to simulate the Long Short-Term Memory mechanism in an LSTM. At each timestep, RecurrentGPT generates a paragraph of text and updates its language-based long-short term memory stored on the hard drive and the prompt, respectively. This recurrence mechanism enables RecurrentGPT to generate texts of arbitrary length without forgetting. Since human users can easily observe and edit the natural language memories, RecurrentGPT is interpretable and enables interactive generation of long text. RecurrentGPT is an initial step towards next-generation computer-assisted writing systems beyond local editing suggestions. In addition to producing AI-generated content (AIGC), we also demonstrate the possibility of using RecurrentGPT as an interactive fiction that directly interacts with consumers. We call this usage of generative models by ``AI As Contents'' (AIAC), which we believe is the next form of conventional AIGC. We further demonstrate the possibility of using RecurrentGPT to create personalized interactive fiction that directly interacts with readers instead of interacting with writers. More broadly, RecurrentGPT demonstrates the utility of borrowing ideas from popular model designs in cognitive science and deep learning for prompting LLMs. Our code is available at https://github.com/aiwaves-cn/RecurrentGPT and an online demo is available at https://www.aiwaves.org/recurrentgpt. |
2023-05-24T00:00:00 | 2305.14314 | QLoRA: Efficient Finetuning of Quantized LLMs | [
"Tim Dettmers",
"Artidoro Pagnoni",
"Ari Holtzman",
"Luke Zettlemoyer"
] | We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA). Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU. QLoRA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information theoretically optimal for normally distributed weights (b) double quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) paged optimziers to manage memory spikes. We use QLoRA to finetune more than 1,000 models, providing a detailed analysis of instruction following and chatbot performance across 8 instruction datasets, multiple model types (LLaMA, T5), and model scales that would be infeasible to run with regular finetuning (e.g. 33B and 65B parameter models). Our results show that QLoRA finetuning on a small high-quality dataset leads to state-of-the-art results, even when using smaller models than the previous SoTA. We provide a detailed analysis of chatbot performance based on both human and GPT-4 evaluations showing that GPT-4 evaluations are a cheap and reasonable alternative to human evaluation. Furthermore, we find that current chatbot benchmarks are not trustworthy to accurately evaluate the performance levels of chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to ChatGPT. We release all of our models and code, including CUDA kernels for 4-bit training. |
|
2023-05-24T00:00:00 | 2305.14201 | Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks | [
"Tiedong Liu",
"Bryan Kian Hsiang Low"
] | We introduce Goat, a fine-tuned LLaMA model that significantly outperforms GPT-4 on a range of arithmetic tasks. Fine-tuned on a synthetically generated dataset, Goat achieves state-of-the-art performance on BIG-bench arithmetic sub-task. In particular, the zero-shot Goat-7B matches or even surpasses the accuracy achieved by the few-shot PaLM-540B. Surprisingly, Goat can achieve near-perfect accuracy on large-number addition and subtraction through supervised fine-tuning only, which is almost impossible with previous pretrained language models, such as Bloom, OPT, GPT-NeoX, etc. We attribute Goat's exceptional performance to LLaMA's consistent tokenization of numbers. To tackle more challenging tasks like large-number multiplication and division, we propose an approach that classifies tasks based on their learnability, and subsequently decomposes unlearnable tasks, such as multi-digit multiplication and division, into a series of learnable tasks by leveraging basic arithmetic principles. We thoroughly examine the performance of our model, offering a comprehensive evaluation of the effectiveness of our proposed decomposition steps. Additionally, Goat-7B can be easily trained using LoRA on a 24GB VRAM GPU, facilitating reproducibility for other researchers. We release our model, dataset, and the Python script for dataset generation. |
|
2023-05-24T00:00:00 | 2305.14233 | Enhancing Chat Language Models by Scaling High-quality Instructional Conversations | [
"Ning Ding",
"Yulin Chen",
"Bokai Xu",
"Yujia Qin",
"Zhi Zheng",
"Shengding Hu",
"Zhiyuan Liu",
"Maosong Sun",
"Bowen Zhou"
] | https://github.com/thunlp/UltraChat | Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT. Scaling the diversity and quality of such data, although straightforward, stands a great chance of leading to improved performance. This paper aims to improve the upper bound of open-source models further. We first provide a systematically designed, diverse, informative, large-scale dataset of instructional conversations, UltraChat, which does not involve human queries. Our objective is to capture the breadth of interactions that a human might have with an AI assistant and employs a comprehensive framework to generate multi-turn conversation iteratively. UltraChat contains 1.5 million high-quality multi-turn dialogues and covers a wide range of topics and instructions. Our statistical analysis of UltraChat reveals its superiority in various key metrics, including scale, average length, diversity, coherence, etc., solidifying its position as a leading open-source dataset. Building upon UltraChat, we fine-tune a LLaMA model to create a powerful conversational model, UltraLLaMA. Our evaluations indicate that UltraLLaMA consistently outperforms other open-source models, including Vicuna, the previously recognized state-of-the-art open-source model. The dataset and the model will be publicly released\url{https://github.com/thunlp/UltraChat}. |
2023-05-24T00:00:00 | 2305.13840 | Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models | [
"Weifeng Chen",
"Jie Wu",
"Pan Xie",
"Hefeng Wu",
"Jiashi Li",
"Xin Xia",
"Xuefeng Xiao",
"Liang Lin"
] | This paper presents a controllable text-to-video (T2V) diffusion model, named Video-ControlNet, that generates videos conditioned on a sequence of control signals, such as edge or depth maps. Video-ControlNet is built on a pre-trained conditional text-to-image (T2I) diffusion model by incorporating a spatial-temporal self-attention mechanism and trainable temporal layers for efficient cross-frame modeling. A first-frame conditioning strategy is proposed to facilitate the model to generate videos transferred from the image domain as well as arbitrary-length videos in an auto-regressive manner. Moreover, Video-ControlNet employs a novel residual-based noise initialization strategy to introduce motion prior from an input video, producing more coherent videos. With the proposed architecture and strategies, Video-ControlNet can achieve resource-efficient convergence and generate superior quality and consistent videos with fine-grained control. Extensive experiments demonstrate its success in various video generative tasks such as video editing and video style transfer, outperforming previous methods in terms of consistency and quality. Project Page: https://controlavideo.github.io/ |
|
2023-05-24T00:00:00 | 2305.13579 | Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach | [
"Yufan Zhou",
"Ruiyi Zhang",
"Tong Sun",
"Jinhui Xu"
] | Recent text-to-image generation models have demonstrated impressive capability of generating text-aligned images with high fidelity. However, generating images of novel concept provided by the user input image is still a challenging task. To address this problem, researchers have been exploring various methods for customizing pre-trained text-to-image generation models. Currently, most existing methods for customizing pre-trained text-to-image generation models involve the use of regularization techniques to prevent over-fitting. While regularization will ease the challenge of customization and leads to successful content creation with respect to text guidance, it may restrict the model capability, resulting in the loss of detailed information and inferior performance. In this work, we propose a novel framework for customized text-to-image generation without the use of regularization. Specifically, our proposed framework consists of an encoder network and a novel sampling method which can tackle the over-fitting problem without the use of regularization. With the proposed framework, we are able to customize a large-scale text-to-image generation model within half a minute on single GPU, with only one image provided by the user. We demonstrate in experiments that our proposed framework outperforms existing methods, and preserves more fine-grained details. |
|
2023-05-24T00:00:00 | 2305.13786 | Perception Test: A Diagnostic Benchmark for Multimodal Video Models | [
"Viorica Pătrăucean",
"Lucas Smaira",
"Ankush Gupta",
"Adrià Recasens Continente",
"Larisa Markeeva",
"Dylan Banarse",
"Skanda Koppula",
"Joseph Heyward",
"Mateusz Malinowski",
"Yi Yang",
"Carl Doersch",
"Tatiana Matejovicova",
"Yury Sulsky",
"Antoine Miech",
"Alex Frechette",
"Hanna Klimczak",
"Raphael Koster",
"Junlin Zhang",
"Stephanie Winkler",
"Yusuf Aytar",
"Simon Osindero",
"Dima Damen",
"Andrew Zisserman",
"João Carreira"
] | https://github.com/deepmind/perception_test | We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e.g. Flamingo, BEiT-3, or GPT-4). Compared to existing benchmarks that focus on computational tasks (e.g. classification, detection or tracking), the Perception Test focuses on skills (Memory, Abstraction, Physics, Semantics) and types of reasoning (descriptive, explanatory, predictive, counterfactual) across video, audio, and text modalities, to provide a comprehensive and efficient evaluation tool. The benchmark probes pre-trained models for their transfer capabilities, in a zero-shot / few-shot or limited finetuning regime. For these purposes, the Perception Test introduces 11.6k real-world videos, 23s average length, designed to show perceptually interesting situations, filmed by around 100 participants worldwide. The videos are densely annotated with six types of labels (multiple-choice and grounded video question-answers, object and point tracks, temporal action and sound segments), enabling both language and non-language evaluations. The fine-tuning and validation splits of the benchmark are publicly available (CC-BY license), in addition to a challenge server with a held-out test split. Human baseline results compared to state-of-the-art video QA models show a significant gap in performance (91.4% vs 43.6%), suggesting that there is significant room for improvement in multimodal video understanding. Dataset, baselines code, and challenge server are available at https://github.com/deepmind/perception_test |
2023-05-24T00:00:00 | 2305.13735 | Aligning Large Language Models through Synthetic Feedback | [
"Sungdong Kim",
"Sanghwan Bae",
"Jamin Shin",
"Soyoung Kang",
"Donghyun Kwak",
"Kang Min Yoo",
"Minjoon Seo"
] | Aligning large language models (LLMs) to human values has become increasingly important as it enables sophisticated steering of LLMs, e.g., making them follow given instructions while keeping them less toxic. However, it requires a significant amount of human demonstrations and feedback. Recently, open-sourced models have attempted to replicate the alignment learning process by distilling data from already aligned LLMs like InstructGPT or ChatGPT. While this process reduces human efforts, constructing these datasets has a heavy dependency on the teacher models. In this work, we propose a novel framework for alignment learning with almost no human labor and no dependency on pre-aligned LLMs. First, we perform reward modeling (RM) with synthetic feedback by contrasting responses from vanilla LLMs with various sizes and prompts. Then, we use the RM for simulating high-quality demonstrations to train a supervised policy and for further optimizing the model with reinforcement learning. Our resulting model, Aligned Language Model with Synthetic Training dataset (ALMoST), outperforms open-sourced models, including Alpaca, Dolly, and OpenAssistant, which are trained on the outputs of InstructGPT or human-annotated instructions. Our 7B-sized model outperforms the 12-13B models in the A/B tests using GPT-4 as the judge with about 75% winning rate on average. |
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2023-05-24T00:00:00 | 2305.13534 | How Language Model Hallucinations Can Snowball | [
"Muru Zhang",
"Ofir Press",
"William Merrill",
"Alisa Liu",
"Noah A. Smith"
] | A major risk of using language models in practical applications is their tendency to hallucinate incorrect statements. Hallucinations are often attributed to knowledge gaps in LMs, but we hypothesize that in some cases, when justifying previously generated hallucinations, LMs output false claims that they can separately recognize as incorrect. We construct three question-answering datasets where ChatGPT and GPT-4 often state an incorrect answer and offer an explanation with at least one incorrect claim. Crucially, we find that ChatGPT and GPT-4 can identify 67% and 87% of their own mistakes, respectively. We refer to this phenomenon as hallucination snowballing: an LM over-commits to early mistakes, leading to more mistakes that it otherwise would not make. |
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2023-05-25T00:00:00 | 2305.15038 | Is GPT-4 a Good Data Analyst? | [
"Liying Cheng",
"Xingxuan Li",
"Lidong Bing"
] | As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc., many data analysts may raise concerns if their jobs will be replaced by AI. This controversial topic has drawn a lot of attention in public. However, we are still at a stage of divergent opinions without any definitive conclusion. Motivated by this, we raise the research question of "is GPT-4 a good data analyst?" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. We propose a framework to tackle the problems by carefully designing the prompts for GPT-4 to conduct experiments. We also design several task-specific evaluation metrics to systematically compare the performance between several professional human data analysts and GPT-4. Experimental results show that GPT-4 can achieve comparable performance to humans. We also provide in-depth discussions about our results to shed light on further studies before we reach the conclusion that GPT-4 can replace data analysts. |
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2023-05-25T00:00:00 | 2305.14878 | Leveraging GPT-4 for Automatic Translation Post-Editing | [
"Vikas Raunak",
"Amr Sharaf",
"Hany Hassan Awadallah",
"Arul Menezes"
] | While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify errors and enhance quality, particularly under critical settings. In this work, we formalize the task of translation post-editing with Large Language Models (LLMs) and explore the use of GPT-4 to automatically post-edit NMT outputs across several language pairs. Our results demonstrate that GPT-4 is adept at translation post-editing and produces meaningful edits even when the target language is not English. Notably, we achieve state-of-the-art performance on WMT-22 English-Chinese, English-German, Chinese-English and German-English language pairs using GPT-4 based post-editing, as evaluated by state-of-the-art MT quality metrics. |
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2023-05-25T00:00:00 | 2305.14564 | PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents | [
"Simeng Sun",
"Yang Liu",
"Shuohang Wang",
"Chenguang Zhu",
"Mohit Iyyer"
] | Strategies such as chain-of-thought prompting improve the performance of large language models (LLMs) on complex reasoning tasks by decomposing input examples into intermediate steps. However, it remains unclear how to apply such methods to reason over long input documents, in which both the decomposition and the output of each intermediate step are non-trivial to obtain. In this work, we propose PEARL, a prompting framework to improve reasoning over long documents, which consists of three stages: action mining, plan formulation, and plan execution. More specifically, given a question about a long document, PEARL decomposes the question into a sequence of actions (e.g., SUMMARIZE, FIND_EVENT, FIND_RELATION) and then executes them over the document to obtain the answer. Each stage of PEARL is implemented via zero-shot or few-shot prompting of LLMs (in our work, GPT-4) with minimal human input. We evaluate PEARL on a challenging subset of the QuALITY dataset, which contains questions that require complex reasoning over long narrative texts. PEARL outperforms zero-shot and chain-of-thought prompting on this dataset, and ablation experiments show that each stage of PEARL is critical to its performance. Overall, PEARL is a first step towards leveraging LLMs to reason over long documents. |
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2023-05-25T00:00:00 | 2305.14540 | LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond | [
"Philippe Laban",
"Wojciech Kryściński",
"Divyansh Agarwal",
"Alexander R. Fabbri",
"Caiming Xiong",
"Shafiq Joty",
"Chien-Sheng Wu"
] | With the recent appearance of LLMs in practical settings, having methods that can effectively detect factual inconsistencies is crucial to reduce the propagation of misinformation and improve trust in model outputs. When testing on existing factual consistency benchmarks, we find that a few large language models (LLMs) perform competitively on classification benchmarks for factual inconsistency detection compared to traditional non-LLM methods. However, a closer analysis reveals that most LLMs fail on more complex formulations of the task and exposes issues with existing evaluation benchmarks, affecting evaluation precision. To address this, we propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits. This new benchmark is 20 times more cost-effective per sample than previous benchmarks and highly reproducible, as we estimate inter-annotator agreement at about 0.9. Most LLMs struggle on SummEdits, with performance close to random chance. The best-performing model, GPT-4, is still 8\% below estimated human performance, highlighting the gaps in LLMs' ability to reason about facts and detect inconsistencies when they occur. |
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2023-05-26T00:00:00 | 2305.16213 | ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation | [
"Zhengyi Wang",
"Cheng Lu",
"Yikai Wang",
"Fan Bao",
"Chongxuan Li",
"Hang Su",
"Jun Zhu"
] | Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., 7.5). We further present various improvements in the design space for text-to-3D such as distillation time schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., 512times512) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops). Further, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic. Project page: https://ml.cs.tsinghua.edu.cn/prolificdreamer/ |
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2023-05-26T00:00:00 | 2305.16291 | Voyager: An Open-Ended Embodied Agent with Large Language Models | [
"Guanzhi Wang",
"Yuqi Xie",
"Yunfan Jiang",
"Ajay Mandlekar",
"Chaowei Xiao",
"Yuke Zhu",
"Linxi Fan",
"Anima Anandkumar"
] | We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize. We open-source our full codebase and prompts at https://voyager.minedojo.org/. |
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2023-05-26T00:00:00 | 2305.15798 | On Architectural Compression of Text-to-Image Diffusion Models | [
"Bo-Kyeong Kim",
"Hyoung-Kyu Song",
"Thibault Castells",
"Shinkook Choi"
] | Exceptional text-to-image (T2I) generation results of Stable Diffusion models (SDMs) come with substantial computational demands. To resolve this issue, recent research on efficient SDMs has prioritized reducing the number of sampling steps and utilizing network quantization. Orthogonal to these directions, this study highlights the power of classical architectural compression for general-purpose T2I synthesis by introducing block-removed knowledge-distilled SDMs (BK-SDMs). We eliminate several residual and attention blocks from the U-Net of SDMs, obtaining over a 30% reduction in the number of parameters, MACs per sampling step, and latency. We conduct distillation-based pretraining with only 0.22M LAION pairs (fewer than 0.1% of the full training pairs) on a single A100 GPU. Despite being trained with limited resources, our compact models can imitate the original SDM by benefiting from transferred knowledge and achieve competitive results against larger multi-billion parameter models on the zero-shot MS-COCO benchmark. Moreover, we demonstrate the applicability of our lightweight pretrained models in personalized generation with DreamBooth finetuning. |
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2023-05-26T00:00:00 | 2305.15779 | Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models | [
"Jooyoung Choi",
"Yunjey Choi",
"Yunji Kim",
"Junho Kim",
"Sungroh Yoon"
] | Text-to-image diffusion models can generate diverse, high-fidelity images based on user-provided text prompts. Recent research has extended these models to support text-guided image editing. While text guidance is an intuitive editing interface for users, it often fails to ensure the precise concept conveyed by users. To address this issue, we propose Custom-Edit, in which we (i) customize a diffusion model with a few reference images and then (ii) perform text-guided editing. Our key discovery is that customizing only language-relevant parameters with augmented prompts improves reference similarity significantly while maintaining source similarity. Moreover, we provide our recipe for each customization and editing process. We compare popular customization methods and validate our findings on two editing methods using various datasets. |