研究提出模型好奇心分类法：好奇式提问仅占17-28%
What Makes a Question Curious? Measuring Model Curiosity in Human-LLM Interactions
一篇研究 LLM 提问质量的文章：好奇式问题只占 17-28%，但能让用户回复更有探索性，做对话产品可以看看。
arXiv 论文 2610.33950 提出“模型好奇心”概念，建立了区分好奇式提问与普通提问的理论分类体系。在 4 个对话数据集上，好奇式问题仅占全部提问的 17-28%。这些问题的后续用户回复更具探索性和知识深度，人工研究显示好奇心在建议、创作和科研场景中能促进探索。
What Makes a Question Curious? Measuring Model Curiosity in Human-LLM Interactions
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.