面向情感支持对话的用户感知主动知识获取
User-Aware Active Knowledge Acquisition for Emotional Support Dialogue
AI总结：
提出用户感知主动知识获取（UKA）框架，通过理论心智不确定性估计和主动学习，在情感支持对话中高效获取用户对齐的对话知识，提升对话质量和用户对齐。
摘要：
情感支持在对话系统中扮演重要角色，其成功取决于在多轮交互中适应用户不断变化且隐含的需求，同时利用大语言模型的强大推理能力。然而，由于用户需求的信号通常微弱、间接，且只能通过多轮交互来消除歧义，现有的情感支持方法往往难以高效获取和泛化相关的对话知识。为弥补这一差距，我们引入了用户感知主动知识获取（UKA），这是一种无梯度的主动对话学习框架，明确表示用户需求的不确定性，并将主动学习融入知识获取和响应选择中。我们提出了一种理论心智不确定性估计机制，使模型能够优先选择响应，从而引发更多信息性的用户反馈。UKA能够在训练期间高效探索用户对齐的对话知识，同时在测试时保持鲁棒性。在多个对话基准和模型架构上的实验表明，我们的方法在对话质量和用户对齐方面始终优于强基线。
英文摘要：
Emotional support plays an important role in dialogue systems, and its success depends on adapting to a user's evolving and implicit needs across multi-turn interactions while leveraging the strong reasoning capacity of large language models. However, since signals about user needs are often weak, indirect, and can only be disambiguated through multi-turn interaction, existing emotional support methods often struggle to acquire and generalize relevant conversational knowledge efficiently. To bridge this gap, we introduce User-Aware Active Knowledge Acquisition (UKA), a gradient-free active dialogue learning framework that explicitly represents uncertainty about user needs and incorporates active learning into both knowledge acquisition and response selection.We propose a Theory-of-Mind uncertainty estimation mechanism that allows the model to prioritize responses, thereby eliciting more informative user feedback. UKA is capable of efficiently exploring user-aligned conversational knowledge during training while maintaining robustness at test time. Experiments across multiple dialogue benchmarks and model architectures demonstrate that our approach consistently outperforms strong baselines in dialogue quality and user alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
