AutoCoder: Enhancing Code Large Language Model with \textscAIEV-Instruct
<|reference_start|>AutoCoder: Enhancing Code Large Language Model with \textscAIEV-Instruct: We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo (April 2024) and GPT-4o in pass@1 on the Human Eval benchmark test ($\mathbf{90.9\%}$ vs. $\mathbf{90.2\%}$). In addition, AutoCoder offers a more versatile code interpreter compared to GPT-4 Turbo and GPT-4o. It's code interpreter can install external packages instead of limiting to built-in packages. AutoCoder's training data is a multi-turn dialogue dataset created by a system combining agent interaction and external code execution verification, a method we term \textbf{\textsc{AIEV-Instruct}} (Instruction Tuning with Agent-Interaction and Execution-Verified). Compared to previous large-scale code dataset generation methods, \textsc{AIEV-Instruct} reduces dependence on proprietary large models and provides execution-validated code dataset. The code and the demo video is available in \url{https://github.com/bin123apple/AutoCoder}.<|reference_end|>
@article{lei2024autocoder:,
    title={AutoCoder: Enhancing Code Large Language Model with
  \textsc{AIEV-Instruct}},
    author={Bin Lei, Yuchen Li, Qiuwu Chen},
    journal={arXiv preprint arXiv:2405.14906},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14906},
    primaryClass={cs.SE cs.AI}
}