75 lines
3.7 KiB
Markdown
75 lines
3.7 KiB
Markdown
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---
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license: llama2
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datasets:
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- xingyaoww/code-act
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language:
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- en
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tags:
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- llm-agent
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pipeline_tag: text-generation
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---
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<h1 align="center"> Executable Code Actions Elicit Better LLM Agents </h1>
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<p align="center">
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<a href="https://github.com/xingyaoww/code-act">💻 Code</a>
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•
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<a href="https://arxiv.org/abs/2402.01030">📃 Paper</a>
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•
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<a href="https://huggingface.co/datasets/xingyaoww/code-act" >🤗 Data (CodeActInstruct)</a>
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•
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<a href="https://huggingface.co/xingyaoww/CodeActAgent-Mistral-7b-v0.1" >🤗 Model (CodeActAgent-Mistral-7b-v0.1)</a>
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•
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<a href="https://chat.xwang.dev/">🤖 Chat with CodeActAgent!</a>
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</p>
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We propose to use executable Python **code** to consolidate LLM agents’ **act**ions into a unified action space (**CodeAct**).
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Integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations (e.g., code execution results) through multi-turn interactions.
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## Why CodeAct?
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Our extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark [M<sup>3</sup>ToolEval](docs/EVALUATION.md) shows that CodeAct outperforms widely used alternatives like Text and JSON (up to 20% higher success rate). Please check our paper for more detailed analysis!
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*Comparison between CodeAct and Text / JSON as action.*
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*Quantitative results comparing CodeAct and {Text, JSON} on M<sup>3</sup>ToolEval.*
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## 📁 CodeActInstruct
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We collect an instruction-tuning dataset CodeActInstruct that consists of 7k multi-turn interactions using CodeAct. Dataset is release at [huggingface dataset 🤗](https://huggingface.co/datasets/xingyaoww/code-act). Please refer to the paper and [this section](#-data-generation-optional) for details of data collection.
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*Dataset Statistics. Token statistics are computed using Llama-2 tokenizer.*
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## 🪄 CodeActAgent
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Trained on **CodeActInstruct** and general conversaions, **CodeActAgent** excels at out-of-domain agent tasks compared to open-source models of the same size, while not sacrificing generic performance (e.g., knowledge, dialog). We release two variants of CodeActAgent:
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- **CodeActAgent-Mistral-7b-v0.1** (recommended, [model link](https://huggingface.co/xingyaoww/CodeActAgent-Mistral-7b-v0.1)): using Mistral-7b-v0.1 as the base model with 32k context window.
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- **CodeActAgent-Llama-7b** ([model link](https://huggingface.co/xingyaoww/CodeActAgent-Llama-2-7b)): using Llama-2-7b as the base model with 4k context window.
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*Evaluation results for CodeActAgent. ID and OD stand for in-domain and out-of-domain evaluation correspondingly. Overall averaged performance normalizes the MT-Bench score to be consistent with other tasks and excludes in-domain tasks for fair comparison.*
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Please check out [our paper](TODO) and [code](https://github.com/xingyaoww/code-act) for more details about data collection, model training, and evaluation.
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## 📚 Citation
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```bibtex
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@misc{wang2024executable,
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title={Executable Code Actions Elicit Better LLM Agents},
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author={Xingyao Wang and Yangyi Chen and Lifan Yuan and Yizhe Zhang and Yunzhu Li and Hao Peng and Heng Ji},
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year={2024},
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eprint={2402.01030},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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