一句话：以 ChatGPT（GPT-3.5 Turbo）与 Gemini Pro 为案例，评估 LLM 简化开源新人上手文档以缓解信息过载的可行性。
开源社区的可持续性依赖持续、多样且高质量的贡献，因此有效的新人上手与留存机制至关重要。多数开源项目依靠 onboarding 文档帮助新人完成首次贡献，但已有研究表明，文档带来的信息过载以及仓库普遍单一语言（英语）的特性，会损害新人体验与上手过程；与此同时，编写与维护这类文档本身也耗费大量精力。作者认为大语言模型可帮助生成更易获取的文档，从而降低信息过载风险。论文以 ChatGPT（GPT-3.5 Turbo）和 Gemini Pro 为案例，评估 LLM 在简化软件 onboarding 文档这一具体方法上的有效性，并在此基础上提出利用 LLM 支持更具可访问性文档创建的更广阔愿景与未来研究方向。
Consistent, diverse, and quality contributions are essential to the sustainability of the open source community. Therefore, it is important that there is infrastructure for effectively onboarding and retaining diverse newcomers to open source software projects. Most often, open source projects rely on onboarding documentation to support newcomers in making their first contributions. Unfortunately, prior studies suggest that information overload from available documentation, along with the predominantly monolingual nature of repositories, can have negative effects on the newcomer experiences and onboarding process. This, coupled with the effort involved in creating and maintaining onboarding documentation, suggest a need for support in creating more accessible documentation. Large language models (LLMs) have shown great potential in providing text transformation support in other domains, and even shown promise in simplifying or generating other kinds of computing artifacts, such as source code and technical documentation. We contend that LLMs can also help make software onboarding documentation more accessible, thereby reducing the potential for information overload. Using ChatGPT (GPT-3.5 Turbo) and Gemini Pro as case studies, we assessed the effectiveness of LLMs for simplifying software onboarding documentation, one method for reducing information overload. We discuss a broader vision for using LLMs to support the creation of more accessible documentation and outline future research directions toward this vision.