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license: apache-2.0
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---
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---
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license: apache-2.0
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datasets:
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- ai2lumos/lumos_web_agent_plan_iterative
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language:
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- en
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tags:
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- language-agent
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- web-agent
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- reasoning
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- planning
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---
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# πͺ Lumos: Language Agents with Unified Formats, Modular Design, and Open-Source LLMs
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<p align="center">
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π<a href="https://allenai.github.io/lumos">[Website]</a>
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π<a href="">[Paper]</a>
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π€<a href="https://huggingface.co/datasets?sort=trending&search=ai2lumos">[Data]</a>
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π€<a href="https://huggingface.co/models?sort=trending&search=ai2lumos">[Model]</a>
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</p>
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We introduce πͺ**Lumos**, Language Agents with **Unified** Formats, **Modular** Design, and **Open-Source** LLMs. **Lumos** unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
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**Lumos** has following features:
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* 𧩠**Modular Architecture**:
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- **Lumos** consists of planning, grounding, and execution modules built based on LLAMA-2-7B.
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* π **Diverse Training Data**:
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- **Lumos** is trained with ~40K high-quality annotations from ground-truth reasoning steps in existing benchmarks with GPT-4.
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* π **Competitive Performance**:
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- π **Lumos** outperforms **GPT-4/3.5-based** agents on complex QA and web agent tasks, and **larger open agents** on maths tasks.
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- π **Lumos** performs better than open agent baseline formulations including **chain-of-thoughts** and **unmodularized** training.
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- π **Lumos** surpasses larger open LLM agents and domain-specific agents on an unseen task, WebShop.
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## Model Overview
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`lumos_web_agent_plan_iterative` is a **planning** module checkpoint finetuned on **web agent** task in **Lumos-Iterative (Lumos-I)** formulation.
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The training annotation is shown below:
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| Training Data | Number |
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|---|---|
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|[`lumos_web_agent_plan_iterative`](https://huggingface.co/datasets/ai2lumos/lumos_web_agent_plan_iterative)|1009|
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## Citation
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If you find this work is relevant with your research, please feel free to cite our work!
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```
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@article{yin2023lumos,
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title={Lumos: Towards Language Agents that are Unified, Modular, and Open Source},
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author={Yin, Da and Brahman, Faeze and Ravichander, Abhilasha and Chandu, Khyathi and Chang, Kai-Wei and Choi, Yejin and Lin, Bill Yuchen},
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year={2023}
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}
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```
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