AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-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 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 \textbf{\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.

2024: Bin Lei, Yuchen Li, Qiuwu Chen



https://arxiv.org/pdf/2405.14906