Kavli Affiliate: Zhuo Li
| First 5 Authors: Kechi Zhang, Zhuo Li, Jia Allen Li, Ge Li, Zhi Jin
| Summary:
Large language models (LLMs) have demonstrated an impressive ability to
generate codes on competitive programming tasks. However, with limited sample
numbers, LLMs still suffer from poor accuracy. Inspired by the process of human
programming, we propose a generate-and-edit approach named Self-Edit that
utilizes execution results of the generated code from LLMs to improve the code
quality on the competitive programming task. We execute the generated code on
the example test case provided in the question and wrap execution results into
a supplementary comment. Utilizing this comment as guidance, our fault-aware
code editor is employed to correct errors in the generated code. We perform
extensive evaluations across two competitive programming datasets with nine
different LLMs. Compared to directly generating from LLMs, our approach can
improve the average of pass@1 by 89% on APPS-dev, 31% on APPS-test, and 48%
on HumanEval over nine popular code generation LLMs with parameter sizes
ranging from 110M to 175B. Compared to other post-processing methods, our
method demonstrates superior accuracy and efficiency.
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