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English(EN) What Makes a Question Curious? Measuring Model Curiosity in Human-LLM Interactions

新研究定义并衡量了大型语言模型交互中的“模型好奇心”

研究人员引入了一个新的框架来衡量人机交互中“模型好奇心”,将其定义为大型语言模型提出能揭示知识差距或假设的认识性问题的能力。在四个数据集中,这类好奇心导向的问题相对罕见,占模型所提所有问题的17-28%。一项人类研究表明,这种好奇心可以促进更深入的探索,尤其是在创意、研究和寻求建议的场景中,这可能对开发更具协作性的AI系统具有启示意义。 AI

影响 引入了一个评估大型语言模型提出探究性问题的框架,可能改善人机协作。

排序理由 在arXiv上发表的研究论文,详细介绍了衡量大型语言模型交互中模型好奇心的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新研究定义并衡量了大型语言模型交互中的“模型好奇心”

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在arXiv上发表的研究论文,详细介绍了衡量大型语言模型交互中模型好奇心的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Angana Borah, Rachana Ramchandar, Inderjeet Nair, Rada Mihalcea ·

    什么让一个问题充满好奇心?衡量人机交互中模型的“好奇心”

    arXiv:2610.33950v1 Announce Type: cross 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, a…