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        <title>Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency
%A Cao, Zhihong
%A Huang, Chen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F cao-huang-2026-clarify
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.833/
%P 17653-17674

Markdown (Informal)

[Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency](https://aclanthology.org/2026.findings-emnlp.833/) (Cao & Huang, Findings 2026)

• Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency (Cao & Huang, Findings 2026)

ACL

• Zhihong Cao and Chen Huang. 2026. Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 17653–17674, Budapest, Hungary. Association for Computational Linguistics.