用大模型生成准确又个性化的代码解释，提升开发与决策效率
From Critique to Clarity: A Pathway to Faithful and Personalized Code Explanations with Large Language Models
- 多智能体协作，结合提示优化与自纠错机制
- 自动与人工评估均证明解释更准确、更贴合用户偏好
- 适合需要理解代码逻辑的开发者和业务决策者
在软件开发中，提供准确且个性化的代码解释对技术人员和业务相关方都至关重要。技术人员可借此提升理解力与问题解决能力，业务方则能获得项目对齐与透明度洞察。尽管潜力巨大，生成此类解释仍常耗时且困难。本文提出一种创新方法，利用大语言模型（LLMs）生成忠实且个性化的代码解释。该方法融合提示增强、自纠错机制、个性化内容定制及与外部工具的交互，由多个LLM智能体协同完成。通过自动与人工评估验证，结果表明该方法不仅能生成高准确度解释，还能根据用户偏好进行定制。研究显示，该方法显著提升了代码解释的质量与相关性，为开发者与利益相关方提供了有力工具。
原文摘要 · Abstract (English)
In the realm of software development, providing accurate and personalized code explanations is crucial for both technical professionals and business stakeholders. Technical professionals benefit from enhanced understanding and improved problem-solving skills, while business stakeholders gain insights into project alignments and transparency. Despite the potential, generating such explanations is often time-consuming and challenging. This paper presents an innovative approach that leverages the advanced capabilities of large language models (LLMs) to generate faithful and personalized code explanations. Our methodology integrates prompt enhancement, self-correction mechanisms, personalized content customization, and interaction with external tools, facilitated by collaboration among multiple LLM agents. We evaluate our approach using both automatic and human assessments, demonstrating that our method not only produces accurate explanations but also tailors them to individual user preferences. Our findings suggest that this approach significantly improves the quality and relevance of code explanations, offering a valuable tool for developers and stakeholders alike.
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