什么让问题显得好奇？测量人机交互中大语言模型的好奇心
What Makes a Question Curious? Measuring Model Curiosity in Human-LLM Interactions
AI 导读
一项新研究提出「模型好奇心」概念及分类体系，区分好奇心导向提问与普通模型提问。在四个对话数据集上，好奇心导向问题占比仅 17-28%，但能引发更具探索性和认知丰富度的用户回应。人类实验显示这类提问可鼓励探索，尤其在建议、创意和研究场景。
正文 · 原文
Abstract:Large language models increasingly act as partners in everyday communication. In these interactions, useful assistance may require not only answering, but also asking the right questions that surface knowledge gaps, assumptions, and alternatives. In this study, we refer to such epistemic question-asking as model curiosity, and introduce a theory-grounded taxonomy that distinguishes curiosity-oriented questions from generic model questioning. Across four conversation datasets, we find that curiosity-oriented questions remain rare (17-28% of all questions), but are associated with more exploratory and epistemically rich subsequent user responses, with varying patterns across datasets. We further conduct a human study to evaluate the usefulness of model curiosity, finding that curiosity can encourage exploration, especially in advice, creative, and research contexts. These findings have implications for building LLMs that ask more selectively to support deeper exploration and human-AI collaboration.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33950 [cs.CL] |
| (or arXiv:2610.33950v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33950 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Angana Borah [view email]
[v1]
Sat, 24 Oct 2026 23:33:21 UTC (1,027 KB)
来源：arXiv cs.AI · arxiv.org