Paper · arXiv 2405.14906
AutoCoder: Enhancing Code Large Language Model with AIEV-Instruct
Bin Lei, Yuchen Li, Qiuwu Chen
AutoCoder, a Large Language Model surpassing GPT-4 Turbo and GPT-4o, uses a novel training method \textsc{AIEV-Instruct} to enhance code interpretation and execution validation.
Abstract
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 (90.9% vs. 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 \textsc{AIEV-Instruct} (Instruction Tuning with Agent-Interaction and Execution-Verified). Compared to previous large-scale code dataset generation methods, AIEV-Instruct reduces dependence on proprietary large models and provides execution-validated code dataset. The code and the demo video is available in https://github.com/bin123apple/AutoCoder.