所需即所得：基于LLM的代码理解助手的心理理论
What You Need is What You Get: Theory of Mind for an LLM-Based Code Understanding Assistant
AI总结：
本文提出一个基于LLM的代码理解对话助手，通过推断用户心理状态实现个性化交互，并在十四名初学者的受试者内研究中验证了其有效性，为相关工具设计提供了见解。
摘要：
越来越多的工具使用大型语言模型（LLMs）来支持开发者的代码理解。然而，开发者在使用此类工具时仍面临若干障碍，包括用自然语言描述其意图的挑战、解读工具输出结果的困难，以及优化有效提示以获取有用信息的难题。在本研究中，我们设计了一个基于LLM的对话式助手，该助手根据推断出的用户心理状态（例如背景知识和经验）提供个性化交互。我们在一项包含十四名初学者的受试者内研究中评估了该方法，以捕捉他们的感知和偏好。我们的结果为希望创建或改进基于LLM的对话式助手以支持初学者进行代码理解的研究人员和工具构建者提供了见解。
英文摘要：
A growing number of tools have used Large Language Models (LLMs) to support developers' code understanding. However, developers still face several barriers to using such tools, including challenges in describing their intent in natural language, interpreting the tool outcome, and refining an effective prompt to obtain useful information. In this study, we designed an LLM-based conversational assistant that provides a personalized interaction based on inferred user mental state (e.g., background knowledge and experience). We evaluate the approach in a within-subject study with fourteen novices to capture their perceptions and preferences. Our results provide insights for researchers and tool builders who want to create or improve LLM-based conversational assistants to support novices in code understanding.
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。
