Instruction tuning plays a pivotal role in Code Large Language Models (Code LLMs) for the task of program synthesis. Presently, two dominant paradigms for collecting tuning data are natural-instruct (human-written) and self-instruct (automatically generated). Natural-instruct includes diverse and correct codes but lacks instruction-code pairs, and exists improper code formats like nested single-line codes. In contrast, self-instruct automatically generates proper paired data. However, it suffers from low diversity due to generating duplicates and cannot ensure the correctness of codes. To bridge the both paradigms, we propose \textbf{Semi-Instruct}. It first converts diverse but improper codes from natural-instruct into proper instruction-code pairs through a method similar to self-instruct. To verify the correctness of generated codes, we design a novel way to construct test cases by generating cases' inputs and executing correct codes from natural-instruct to get outputs. Finally, diverse and correct instruction-code pairs are retained for instruction tuning. Experiments show that semi-instruct is significantly better than natural-instruct and self-instruct. Furthermore, the performance steadily improves as data scale increases.

指令调优在大规模代码语言模型（Code LLMs）中对程序合成任务至关重要。本文提出一种名为Semi-Instruct的方法，通过将自然指令中不规范代码转化为正确的指令-代码对，并设计了一种新颖的测试用例构建方法来验证生成代码的正确性。实验结果显示，Semi-Instruct方法在数据规模增加时性能稳定提高。

半指导：连接自然指导和自我指导的大型编码语言模型