剖析大模型开源项目常见问题及根因，给出实用解决方案。
Demystifying Issues, Causes and Solutions in LLM Open-Source Projects
- 通过分析15个开源项目994个问题，识别核心挑战
- 模型问题最常见，配置与连接问题是主要诱因
- 优化模型是主流解决方式，对开发者有直接参考价值
随着大语言模型（LLMs）的发展，越来越多开源项目将LLM作为核心功能组件。尽管学术界和工业界对LLM的研究与应用广泛关注，但尚无专门研究探讨实践者在开发和使用LLM开源软件时面临的挑战、根本原因及潜在解决方案。为填补这一空白，我们开展了一项实证研究，收集15个LLM开源项目的全部关闭问题，并筛选出符合条件的议题。从中随机选取994个问题作为样本进行数据提取与分析，以理解普遍存在的问题、其深层原因及可行对策。研究结果表明：(1) 模型问题是最常见的挑战；(2) 模型缺陷、配置与连接问题、功能与方法缺陷是主要成因；(3) 优化模型是应用最广泛的应对策略。基于研究发现，我们为项目实践者与研究者提供了切实可行的建议。
原文摘要 · Abstract (English)
With the advancements of Large Language Models (LLMs), an increasing number of open-source software projects are using LLMs as their core functional component. Although research and practice on LLMs are capturing considerable interest, no dedicated studies explored the challenges faced by practitioners of LLM open-source projects, the causes of these challenges, and potential solutions. To fill this research gap, we conducted an empirical study to understand the issues that practitioners encounter when developing and using LLM open-source software, the possible causes of these issues, and potential solutions. We collected all closed issues from 15 LLM open-source projects and labelled issues that met our requirements. We then randomly selected 994 issues from the labelled issues as the sample for data extraction and analysis to understand the prevalent issues, their underlying causes, and potential solutions. Our study results show that (1) Model Issue is the most common issue faced by practitioners, (2) Model Problem, Configuration and Connection Problem, and Feature and Method Problem are identified as the most frequent causes of the issues, and (3) Optimize Model is the predominant solution to the issues. Based on the study results, we provide implications for practitioners and researchers of LLM open-source projects.
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