Recent advancements in open-source code large language models (LLMs) have demonstrated remarkable coding abilities by fine-tuning on the data generated from powerful closed-source LLMs such as GPT-3.5 and GPT-4 for instruction tuning. This paper explores how to further improve an instruction-tuned code LLM by generating data from itself rather than querying closed-source LLMs. Our key observation is the misalignment between the translation of formal and informal languages: translating formal language (i.e., code) to informal language (i.e., natural language) is more straightforward than the reverse. Based on this observation, we propose INVERSE-INSTRUCT, which summarizes instructions from code snippets instead of the reverse. Specifically, given an instruction tuning corpus for code and the resulting instruction-tuned code LLM, we ask the code LLM to generate additional high-quality instructions for the original corpus through code summarization and self-evaluation. Then, we fine-tune the base LLM on the combination of the original corpus and the self-generated one, which yields a stronger instruction-tuned LLM. We present a series of code LLMs named InverseCoder, which surpasses the performance of the original code LLMs on a wide range of benchmarks, including Python text-to-code generation, multilingual coding, and data-science code generation.

通过在自身生成的数据上fine-tune，而非查询封闭源语言模型，进一步改进了指令调优的代码大型语言模型。借鉴正式语言和非正式语言翻译的错位现象，引入了INVERSE-INSTRUCT，从代码片段中总结指令。通过代码概括和自我评估，改进了基于指令调优语言模型的性能，其通过Python文本生成代码、多语言编码以及数据科学代码生成等多个基准测试中展现了优异表现。

InverseCoder: 发挥逆向指令调谐代码语言模型的能力与逆向指令实现