Creative Robot Tool Use with Large Language Models
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2310.13065
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Published
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8
Coding
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CodeCoT and Beyond: Learning to Program and Test like a Developer
Paper • 2308.08784 • Published • 5 -
Lemur: Harmonizing Natural Language and Code for Language Agents
Paper • 2310.06830 • Published • 30 -
CodePlan: Repository-level Coding using LLMs and Planning
Paper • 2309.12499 • Published • 73 -
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
Paper • 2310.03714 • Published • 30 -
Prompt2Model: Generating Deployable Models from Natural Language Instructions
Paper • 2308.12261 • Published • 1 -
AskIt: Unified Programming Interface for Programming with Large Language Models
Paper • 2308.15645 • Published • 2 -
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
Paper • 2310.03731 • Published • 29 -
SCREWS: A Modular Framework for Reasoning with Revisions
Paper • 2309.13075 • Published • 15 -
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Paper • 2309.05653 • Published • 10 -
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving
Paper • 2309.17452 • Published • 3 -
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification
Paper • 2308.07921 • Published • 22 -
Octopus: Embodied Vision-Language Programmer from Environmental Feedback
Paper • 2310.08588 • Published • 34 -
CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules
Paper • 2310.08992 • Published • 10 -
Ranking LLM-Generated Loop Invariants for Program Verification
Paper • 2310.09342 • Published • 2 -
UI Layout Generation with LLMs Guided by UI Grammar
Paper • 2310.15455 • Published • 2 -
LMDX: Language Model-based Document Information Extraction and Localization
Paper • 2309.10952 • Published • 65 -
Code Llama: Open Foundation Models for Code
Paper • 2308.12950 • Published • 22 -
CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion
Paper • 2310.11248 • Published • 3 -
Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?
Paper • 2309.07804 • Published • 2 -
CAT-LM: Training Language Models on Aligned Code And Tests
Paper • 2310.01602 • Published • 1 -
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Paper • 2310.06770 • Published • 4 -
Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code
Paper • 2303.08033 • Published • 1 -
Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation
Paper • 2308.01240 • Published • 2 -
OctoPack: Instruction Tuning Code Large Language Models
Paper • 2308.07124 • Published • 28 -
Can Programming Languages Boost Each Other via Instruction Tuning?
Paper • 2308.16824 • Published • 9 -
StarCoder: may the source be with you!
Paper • 2305.06161 • Published • 29 -
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
Paper • 2308.10462 • Published • 1 -
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning
Paper • 2308.11148 • Published • 2 -
ViperGPT: Visual Inference via Python Execution for Reasoning
Paper • 2303.08128 • Published • 2 -
Visual Programming: Compositional visual reasoning without training
Paper • 2211.11559 • Published • 1 -
Modular Visual Question Answering via Code Generation
Paper • 2306.05392 • Published • 2 -
LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models
Paper • 2309.09506 • Published • 14 -
Evaluating and Explaining Large Language Models for Code Using Syntactic Structures
Paper • 2308.03873 • Published • 1 -
CodeFusion: A Pre-trained Diffusion Model for Code Generation
Paper • 2310.17680 • Published • 69 -
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
Paper • 2207.01780 • Published • 1 -
RLTF: Reinforcement Learning from Unit Test Feedback
Paper • 2307.04349 • Published • 4 -
Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation
Paper • 2310.18628 • Published • 7 -
Safurai 001: New Qualitative Approach for Code LLM Evaluation
Paper • 2309.11385 • Published • 2 -
Large Language Models for Software Engineering: A Systematic Literature Review
Paper • 2308.10620 • Published • 1 -
Software Testing with Large Language Model: Survey, Landscape, and Vision
Paper • 2307.07221 • Published • 1 -
ComputeGPT: A computational chat model for numerical problems
Paper • 2305.06223 • Published • 1 -
Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning
Paper • 2309.10814 • Published • 3 -
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models
Paper • 2305.18507 • Published • 1 -
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
Paper • 2211.12588 • Published • 3 -
Structured Chain-of-Thought Prompting for Code Generation
Paper • 2305.06599 • Published • 1 -
Improving ChatGPT Prompt for Code Generation
Paper • 2305.08360 • Published • 1 -
Pair Programming with Large Language Models for Sampling and Estimation of Copulas
Paper • 2303.18116 • Published • 1 -
EcoAssistant: Using LLM Assistant More Affordably and Accurately
Paper • 2310.03046 • Published • 5 -
CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
Paper • 2309.17428 • Published • 1 -
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation
Paper • 2310.10698 • Published • 1 -
Test-Case-Driven Programming Understanding in Large Language Models for Better Code Generation
Paper • 2309.16120 • Published • 1 -
The Program Testing Ability of Large Language Models for Code
Paper • 2310.05727 • Published • 1 -
ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification
Paper • 2310.10996 • Published • 2 -
DocPrompting: Generating Code by Retrieving the Docs
Paper • 2207.05987 • Published • 1 -
Demystifying GPT Self-Repair for Code Generation
Paper • 2306.09896 • Published • 19 -
Towards an Understanding of Large Language Models in Software Engineering Tasks
Paper • 2308.11396 • Published • 1 -
Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning
Paper • 2305.18170 • Published • 2 -
Impact of Large Language Models on Generating Software Specifications
Paper • 2306.03324 • Published • 2 -
Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?
Paper • 2306.01754 • Published • 1 -
Fault-Aware Neural Code Rankers
Paper • 2206.03865 • Published • 1 -
An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation
Paper • 2302.06527 • Published • 1 -
Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing
Paper • 2308.16557 • Published • 1 -
A Static Evaluation of Code Completion by Large Language Models
Paper • 2306.03203 • Published • 3 -
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors
Paper • 2305.05711 • Published • 1 -
Few-shot training LLMs for project-specific code-summarization
Paper • 2207.04237 • Published • 1 -
Improving Few-Shot Prompts with Relevant Static Analysis Products
Paper • 2304.06815 • Published • 1 -
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning
Paper • 2304.11384 • Published • 1 -
Repository-Level Prompt Generation for Large Language Models of Code
Paper • 2206.12839 • Published • 2 -
A Systematic Evaluation of Large Language Models of Code
Paper • 2202.13169 • Published • 1 -
Private-Library-Oriented Code Generation with Large Language Models
Paper • 2307.15370 • Published • 1 -
CodeTF: One-stop Transformer Library for State-of-the-art Code LLM
Paper • 2306.00029 • Published • 2 -
Bias Assessment and Mitigation in LLM-based Code Generation
Paper • 2309.14345 • Published • 1 -
A Simple, Yet Effective Approach to Finding Biases in Code Generation
Paper • 2211.00609 • Published • 1 -
Execution-Based Evaluation for Open-Domain Code Generation
Paper • 2212.10481 • Published • 1 -
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Paper • 2203.13474 • Published • 1 -
Improving Code Generation by Training with Natural Language Feedback
Paper • 2303.16749 • Published • 1 -
Large Language Models of Code Fail at Completing Code with Potential Bugs
Paper • 2306.03438 • Published • 2 -
How Effective Are Neural Networks for Fixing Security Vulnerabilities
Paper • 2305.18607 • Published • 2 -
Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair
Paper • 2309.00608 • Published • 2 -
Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions
Paper • 2304.03816 • Published • 1 -
Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering
Paper • 2304.07840 • Published • 1 -
Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models
Paper • 2302.04662 • Published • 1 -
Frustrated with Code Quality Issues? LLMs can Help!
Paper • 2309.12938 • Published • 1 -
FLAG: Finding Line Anomalies (in code) with Generative AI
Paper • 2306.12643 • Published • 1 -
The potential of LLMs for coding with low-resource and domain-specific programming languages
Paper • 2307.13018 • Published • 1 -
Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs
Paper • 2308.09895 • Published • 1 -
Large Language Models Meet NL2Code: A Survey
Paper • 2212.09420 • Published • 1 -
ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation
Paper • 2308.01861 • Published • 1 -
ToolCoder: Teach Code Generation Models to use API search tools
Paper • 2305.04032 • Published • 1 -
Out of the BLEU: how should we assess quality of the Code Generation models?
Paper • 2208.03133 • Published • 2 -
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Paper • 2311.02303 • Published • 4 -
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Paper • 2306.08568 • Published • 28 -
ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation
Paper • 2311.00272 • Published • 9 -
CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models
Paper • 2309.01940 • Published • 1 -
Exploring Large Language Models for Code Explanation
Paper • 2310.16673 • Published • 1 -
Prompt Engineering or Fine Tuning: An Empirical Assessment of Large Language Models in Automated Software Engineering Tasks
Paper • 2310.10508 • Published • 1 -
A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repair
Paper • 2310.08879 • Published • 1 -
Large Language Model-Aware In-Context Learning for Code Generation
Paper • 2310.09748 • Published • 1 -
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis
Paper • 2310.03173 • Published • 1 -
SteloCoder: a Decoder-Only LLM for Multi-Language to Python Code Translation
Paper • 2310.15539 • Published • 1 -
T5APR: Empowering Automated Program Repair across Languages through Checkpoint Ensemble
Paper • 2309.15742 • Published • 1 -
InstructCoder: Empowering Language Models for Code Editing
Paper • 2310.20329 • Published • 2 -
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
Paper • 2305.06156 • Published • 2 -
Constructing Multilingual Code Search Dataset Using Neural Machine Translation
Paper • 2306.15604 • Published • 1 -
Neural Machine Translation for Code Generation
Paper • 2305.13504 • Published • 1 -
On Learning Meaningful Code Changes via Neural Machine Translation
Paper • 1901.09102 • Published • 1 -
Dynamically Relative Position Encoding-Based Transformer for Automatic Code Edit
Paper • 2205.13522 • Published • 1 -
Code Translation with Compiler Representations
Paper • 2207.03578 • Published • 1 -
CoCoSoDa: Effective Contrastive Learning for Code Search
Paper • 2204.03293 • Published • 1 -
ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning
Paper • 2301.09072 • Published • 1 -
Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models
Paper • 2303.06233 • Published • 1 -
One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization
Paper • 2303.15822 • Published • 1 -
CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models
Paper • 2206.00052 • Published • 1 -
Explainable AI for Pre-Trained Code Models: What Do They Learn? When They Do Not Work?
Paper • 2211.12821 • Published • 1 -
Benchmarking Language Models for Code Syntax Understanding
Paper • 2210.14473 • Published • 1 -
Are Code Pre-trained Models Powerful to Learn Code Syntax and Semantics?
Paper • 2212.10017 • Published • 1 -
Diet Code Is Healthy: Simplifying Programs for Pre-trained Models of Code
Paper • 2206.14390 • Published • 1 -
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification
Paper • 2305.04940 • Published • 1 -
TRACED: Execution-aware Pre-training for Source Code
Paper • 2306.07487 • Published • 1 -
Method-Level Bug Severity Prediction using Source Code Metrics and LLMs
Paper • 2309.03044 • Published • 1 -
WELL: Applying Bug Detectors to Bug Localization via Weakly Supervised Learning
Paper • 2305.17384 • Published • 1 -
BigIssue: A Realistic Bug Localization Benchmark
Paper • 2207.10739 • Published • 1 -
RLocator: Reinforcement Learning for Bug Localization
Paper • 2305.05586 • Published • 1 -
A Survey of Learning-based Automated Program Repair
Paper • 2301.03270 • Published • 1 -
GAMMA: Revisiting Template-based Automated Program Repair via Mask Prediction
Paper • 2309.09308 • Published • 1 -
Too Few Bug Reports? Exploring Data Augmentation for Improved Changeset-based Bug Localization
Paper • 2305.16430 • Published • 1 -
Split, Encode and Aggregate for Long Code Search
Paper • 2208.11271 • Published • 1 -
CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search
Paper • 2305.11626 • Published • 1 -
Searching by Code: a New SearchBySnippet Dataset and SnippeR Retrieval Model for Searching by Code Snippets
Paper • 2305.11625 • Published • 1 -
ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages
Paper • 2212.06742 • Published • 2 -
Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey
Paper • 2308.01191 • Published • 1 -
Communicative Agents for Software Development
Paper • 2307.07924 • Published • 3 -
Assessing the Use of AutoML for Data-Driven Software Engineering
Paper • 2307.10774 • Published • 1 -
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure
Paper • 2210.04633 • Published • 1 -
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
Paper • 2304.05216 • Published • 1 -
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
Paper • 2310.02304 • Published • 1 -
Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming
Paper • 2306.05153 • Published • 1 -
"Teach AI How to Code": Using Large Language Models as Teachable Agents for Programming Education
Paper • 2309.14534 • Published • 2 -
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
Paper • 2306.04930 • Published • 3 -
A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges
Paper • 2303.17125 • Published • 1 -
LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression
Paper • 2309.14021 • Published • 1 -
Prompting with Pseudo-Code Instructions
Paper • 2305.11790 • Published • 2 -
Magicoder: Source Code Is All You Need
Paper • 2312.02120 • Published • 79 -
Evolution through Large Models
Paper • 2206.08896 • Published • 1 -
LLM360: Towards Fully Transparent Open-Source LLMs
Paper • 2312.06550 • Published • 56 -
Understanding HTML with Large Language Models
Paper • 2210.03945 • Published • 1 -
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding
Paper • 2306.06094 • Published • 1 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 49 -
Neural Rankers for Code Generation via Inter-Cluster Modeling
Paper • 2311.03366 • Published • 1 -
LLM-Assisted Code Cleaning For Training Accurate Code Generators
Paper • 2311.14904 • Published • 3 -
Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
Paper • 2312.04474 • Published • 29 -
A Survey on Language Models for Code
Paper • 2311.07989 • Published • 21 -
CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution
Paper • 2401.03065 • Published • 10 -
Is Model Attention Aligned with Human Attention? An Empirical Study on Large Language Models for Code Generation
Paper • 2306.01220 • Published • 1 -
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
Paper • 2310.06266 • Published • 1 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 2 -
Code as Policies: Language Model Programs for Embodied Control
Paper • 2209.07753 • Published • 1 -
Large Language Models for Compiler Optimization
Paper • 2309.07062 • Published • 22 -
Program Merge Conflict Resolution via Neural Transformers
Paper • 2109.00084 • Published • 1 -
HuggingFaceM4/WebSight
Viewer • Updated • 2.75M • 9.59k • 329 -
AceCoder: Utilizing Existing Code to Enhance Code Generation
Paper • 2303.17780 • Published • 1 -
SkCoder: A Sketch-based Approach for Automatic Code Generation
Paper • 2302.06144 • Published • 1 -
What Makes Good In-context Demonstrations for Code Intelligence Tasks with LLMs?
Paper • 2304.07575 • Published • 1 -
The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks
Paper • 2305.09082 • Published • 1 -
RestGPT: Connecting Large Language Models with Real-World RESTful APIs
Paper • 2306.06624 • Published • 1 -
Leveraging Large Language Models to Improve REST API Testing
Paper • 2312.00894 • Published • 2 -
Type-Directed Program Synthesis for RESTful APIs
Paper • 2203.16697 • Published • 1 -
Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
Paper • 2401.08500 • Published • 5 -
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
Paper • 2305.04106 • Published • 1 -
LEVER: Learning to Verify Language-to-Code Generation with Execution
Paper • 2302.08468 • Published • 1 -
StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback
Paper • 2402.01391 • Published • 41 -
Improving Natural Language Capability of Code Large Language Model
Paper • 2401.14242 • Published • 1 -
Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models
Paper • 2401.00788 • Published • 21 -
Leveraging Large Language Models for Automated Proof Synthesis in Rust
Paper • 2311.03739 • Published • 5 -
Guiding Language Models of Code with Global Context using Monitors
Paper • 2306.10763 • Published • 7 -
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Paper • 2402.04858 • Published • 14 -
MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline
Paper • 2401.08190 • Published -
Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming
Paper • 2402.14261 • Published • 10 -
StarCoder 2 and The Stack v2: The Next Generation
Paper • 2402.19173 • Published • 134 -
PYInfer: Deep Learning Semantic Type Inference for Python Variables
Paper • 2106.14316 • Published -
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Paper • 2404.02575 • Published • 47 -
Advancing LLM Reasoning Generalists with Preference Trees
Paper • 2404.02078 • Published • 44 -
NExT: Teaching Large Language Models to Reason about Code Execution
Paper • 2404.14662 • Published • 4 -
MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning
Paper • 2405.07551 • Published -
PECC: Problem Extraction and Coding Challenges
Paper • 2404.18766 • Published -
Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective
Paper • 2404.07549 • Published -
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
Paper • 2402.09136 • Published • 1 -
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
Paper • 2402.10176 • Published • 34 -
Grounding Data Science Code Generation with Input-Output Specifications
Paper • 2402.08073 • Published -
SemCoder: Training Code Language Models with Comprehensive Semantics
Paper • 2406.01006 • Published -
AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology
Paper • 2406.11912 • Published • 26 -
From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging
Paper • 2410.01215 • Published • 30