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Executable Code Actions Elicit Better LLM Agents

πŸ’» Code β€’ πŸ“ƒ Paper β€’ πŸ€— Data (CodeActInstruct) β€’ πŸ€— Model (CodeActAgent-Mistral-7b-v0.1) β€’ πŸ€– Chat with CodeActAgent!

We propose to use executable Python code to consolidate LLM agents’ actions into a unified action space (CodeAct). 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.


Why CodeAct?

Our extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark M3ToolEval 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!

Comparison between CodeAct and Text/JSON Comparison between CodeAct and Text / JSON as action.

Comparison between CodeAct and Text/JSON Quantitative results comparing CodeAct and {Text, JSON} on M3ToolEval.

πŸ“ CodeActInstruct

We collect an instruction-tuning dataset CodeActInstruct that consists of 7k multi-turn interactions using CodeAct. Dataset is release at huggingface dataset πŸ€—. Please refer to the paper and this section for details of data collection.

Data Statistics Dataset Statistics. Token statistics are computed using Llama-2 tokenizer.

πŸͺ„ CodeActAgent

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:

  • CodeActAgent-Mistral-7b-v0.1 (recommended, model link): using Mistral-7b-v0.1 as the base model with 32k context window.
  • CodeActAgent-Llama-7b (model link): using Llama-2-7b as the base model with 4k context window.

Model Performance 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.

Please check out our paper and code for more details about data collection, model training, and evaluation.

πŸ“š Citation

      title={Executable Code Actions Elicit Better LLM Agents}, 
      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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Dataset used to train xingyaoww/CodeActAgent-Mistral-7b-v0.1