AI 中文总结
研究LLM代码解释能否适应不同问题解决者需求，基于包容性设计方法开发提示，用自然语言处理技术从六个LLM生成代码解释，发现13种语言适应分类法，贡献两种问题解决适应方法。
AI 中文摘要
大语言模型（LLM）代码解释可辅助人们解决代码相关问题，但人们有多样的问题解决风格。此前虽有研究关注LLM输出如何适应年龄或专业知识，但未探讨其代码解释如何适应问题解决风格。为填补这一空白，我们基于既定的包容性设计方法开发提示，从六个开放权重的LLM生成1072条代码解释。运用自然语言处理技术，发现了13种语言适应的分类法，每种适应都有文献、提示或LLM输出的证据支持，还展示了哪些LLM更频繁地调整代码解释。本文首次研究LLM代码解释中的问题解决风格适应，贡献了两种问题解决适应方法：每种适应的声明性语句和10个问题解决风格提示。
英文摘要
Large language model (LLM) code explanations can support people in solving code-related problems, yet prior work has shown that people have diverse problem-solving styles. If explanations fail to meet people's problem-solving needs, they may be less productive in their occupations and miss opportunities to learn and grow. Although some research has examined how LLMs can adapt their outputs to a user's age or expertise, no prior work has examined how LLMs can adapt their code explanations to people's problem-solving styles. To address this gap, we developed prompts from an established inclusive design method that considers 5 types of problem-solving styles, and we generated 1,072 code explanations from six open-weight LLMs. Using natural language processing techniques, we uncovered a taxonomy of 13 linguistic adaptations, with each adaptation supported by evidence from the literature, the prompts, or the LLMs' outputs. They also show which LLMs adapted their code explanations more frequently than others. This paper is the first to investigate problem-solving style adaptations in LLM code explanation, contributing two problem-solving adaptation approaches: declarative statements for each adaptation and 10 problem-solving style prompts.
CommentsTo be published in AAAI/ACM conference on AI, Ethics, and Society (AIES 2026)
