让对话智能体主动判断用户水平，动态调整回答难度。
Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency
- 通过大模型自博弈生成提问策略，主动探查用户专业程度。
- 在多个数据集上显著提升响应适配度，优于现有方法。
- 适合需要个性化知识服务的场景，如教育、医疗咨询。
在信息获取场景中，对话智能体正从被动工具演变为主动个性化的助手。关键挑战在于根据用户独特需求和预期，动态调整交互策略。现有研究多关注主动澄清查询模糊性，而本文聚焦于主动澄清用户专业水平，以优化回答的可理解性。我们发现，现有智能体仅凭查询难以准确判断用户专业程度，导致无法动态调整响应。为此，提出PASSING框架，通过大模型自博弈生成‘问什么’和‘怎么问’的策略，主动引导用户披露专业水平。大量实验表明，该方法在多个基准上均取得显著优势。我们认为，PASSING是构建更以人为本对话系统的关键一步。
原文摘要 · Abstract (English)
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.
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