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About this paper
Dong Huang, Qingwen Bu, Yuhao Qing, Heming Cui
CodeCoT introduces an iterative self-examination framework that addresses syntax errors in Chain-of-Thought (CoT) based code generation, significantly improving `pass@1` on HumanEval from 57.3% to 79.3% and on MBPP from 52.2% to 89.5% by feeding execution feedback to the LLM.
Key insights
- The combination of Chain-of-Thought (CoT) reasoning and iterative execution feedback provides a synergistic effect, enabling the LLM to generate both logically sound and syntactically correct code.
- CoT prompting significantly enhances the quality and coverage of LLM-generated test cases, which are critical for effective self-examination and error correction.
- Iterative self-examination effectively reduces runtime errors; for instance, 5 steps nearly eliminated runtime errors, dropping the rate to 2% for HumanEval and 1% for MBPP.
Summary by alphaXiv.
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