发表机构
School of Computing and Data Science, The University of Hong Kong; Institute of Data Science, National University of Singapore; College of Computer Science, Sichuan University（香港大学计算与数据科学学院; 新加坡国立大学数据科学研究所; 四川大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对现有对话智能体无法仅通过查询判断用户专业能力的缺陷，提出PASSING方法，通过LLM自博弈生成的询问策略主动明确用户专业能力，以调整响应提升用户理解，实验显示该方法具有优越性。
AI 中文摘要
在信息获取场景中，对话智能体正从被动工具向主动、个性化助手演进，这一演进的关键在于能够根据用户的独特需求与期望调整策略性交互。与现有聚焦于主动澄清查询歧义的研究不同，本研究以明确用户专业能力为核心，从而调整响应以提升用户的理解效果。我们发现，现有智能体难以仅通过查询判断用户专业能力，这一缺陷阻碍了其动态调整响应的能力。为解决该问题，我们提出PASSING，通过针对性询问让智能体主动明确用户的专业能力，该功能由大语言模型（LLM）自博弈生成的“问什么”与“如何问”策略实现。大量实验表明我们的方法具有优越性，我们认为PASSING是构建更以人为本的对话智能体的关键一步。
英文摘要
In the context of information seeking, conversational agents are undergoing an evolution from reactive tools to proactive, personalized assistants. A critical aspect of this evolution is the ability to tailor strategic interactions to a user's unique needs and expectations. Unlike existing studies that focus on proactively clarifying query ambiguities, we center on clarifying the user's expertise in order to tailor responses for better user comprehension. We find that existing agents struggle to determine user expertise from queries alone, a limitation that prevents them from dynamically adapting their responses. To address this gap, we introduce PASSING to empower the agent to proactively clarify a user's expertise through targeted inquiries. This is achieved by our What-to-ask and How-to-ask strategies, induced by LLM self-play. Our extensive experiments also show our superiority. We believe that PASSING represents a crucial step towards creating more human-centric conversational agents.
CommentsFindings of EMNLP 2026
