从批判到清晰：利用大型语言模型生成忠实且个性化代码解释的路径
From Critique to Clarity: A Pathway to Faithful and Personalized Code Explanations with Large Language Models
AI总结：
本文提出一种利用多个LLM代理协作的方法，结合提示增强、自我纠正、个性化定制及外部工具交互，生成忠实且个性化的代码解释，显著提升了解释的质量与相关性。
摘要：
在软件开发领域，提供准确且个性化的代码解释对技术专业人员和业务利益相关者都至关重要。技术专业人员受益于理解能力的提升和解决问题技能的改进，而业务利益相关者则能洞察项目对齐情况和透明度。尽管有此潜力，但生成此类解释通常既耗时又具挑战性。本文提出了一种创新方法，利用大型语言模型（LLMs）的先进能力来生成忠实且个性化的代码解释。我们的方法整合了提示增强、自我纠正机制、个性化内容定制以及与外部工具的交互，这些均通过多个LLM代理间的协作来实现。我们使用自动和人工评估来评估该方法，结果表明我们的方法不仅能产生准确的解释，还能根据个人用户偏好进行定制。研究结果表明，该方法显著提升了代码解释的质量和相关性，为开发者和利益相关者提供了有价值的工具。
英文摘要：
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.
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。
