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.

“机器音译是基于语音相似性自动将文本从一种语言转换为另一种语言的过程,它是机器翻译的一个子任务,侧重于语音信息的翻译。音译后可知道源单词在另一种语言中的发音,使不熟悉源语言的人更容易理解该语言,有益于消除语言和拼写障碍。机器音译在多语言文本处理、语料库对齐、信息抽取等自然语言应用中发挥着重要作用。本文阐述了目前机器音译任务中存在的挑战,对主要的音译方法进行了剖析、分类和整理,对音译数据集进行了罗列汇总,并列出了常用的音译效果评价指标,最后对该领域目前存在的问题进行了说明并对音译学的未来进行了展望。本文以期对进入该领域的新人提供快速的入门指南,或供其他研究者参考。”