LARCH：基于大语言模型的启发式自动README生成方法
LARCH: Large Language Model-based Automatic Readme Creation with Heuristics
AI总结：
针对大语言模型难以从大量代码生成准确README的问题，提出结合启发式与弱监督识别代表性代码的LARCH工具，实验证明其生成效果优于基线，已开源并提供多端使用接口。
摘要：
编写README是软件开发的关键环节，对程序代码的管理和复用至关重要。尽管这是许多开发者的痛点，但即便大语言模型（LLM）近年来取得进展，自动生成README仍具挑战，因为该任务需要从数千行代码中生成抽象描述。在这篇演示论文中，我们证明若能识别出代表代码仓库的代码片段，LLM就能够生成连贯且事实准确的README。基于这一发现，我们开发了LARCH（基于LLM的启发式自动README生成工具），它结合启发式方法与弱监督实现代表性代码识别。通过人工评估与自动化评估，我们证实LARCH在大多数情况下能生成连贯且事实准确的README，性能优于不依赖代表性代码识别的基线方法。我们已将LARCH开源，并提供跨平台的Visual Studio Code界面与命令行界面，可通过https://github.com/hitachi-nlp/larch获取。展示LARCH功能的演示视频可在https://youtu.be/ZUKkh5ED-O4查看。
英文摘要：
Writing a readme is a crucial aspect of software development as it plays a vital role in managing and reusing program code. Though it is a pain point for many developers, automatically creating one remains a challenge even with the recent advancements in large language models (LLMs), because it requires generating an abstract description from thousands of lines of code. In this demo paper, we show that LLMs are capable of generating a coherent and factually correct readmes if we can identify a code fragment that is representative of the repository. Building upon this finding, we developed LARCH (LLM-based Automatic Readme Creation with Heuristics) which leverages representative code identification with heuristics and weak supervision. Through human and automated evaluations, we illustrate that LARCH can generate coherent and factually correct readmes in the majority of cases, outperforming a baseline that does not rely on representative code identification. We have made LARCH open-source and provided a cross-platform Visual Studio Code interface and command-line interface, accessible at https://github.com/hitachi-nlp/larch. A demo video showcasing LARCH's capabilities is available at https://youtu.be/ZUKkh5ED-O4.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
