让对话机器人学会根据语境调整回应节奏，更像懂人的倾听者。
Hear You in Silence: Designing for Active Listening in Human Interaction with Conversational Agents Using Context-Aware Pacing
- 基于人类倾听行为设计五种动态响应节奏策略
- 实验显示用户感知更自然，自我暴露更深，信任感更高
- 适合情感陪伴、心理咨询类AI系统优化
在人际对话中，共情交流依赖于微妙的时间线索来表达是否专注倾听。当前对话系统普遍采用固定回应节奏，忽略了主动倾听的时序特征。本研究通过分析10个真实倾听案例，提炼出五种情境感知的响应节奏策略：反射性沉默、促进性沉默、共情性沉默、留白空间和即时回应。在两组独立样本（N=50）的对照实验中，对比关系支持与职业支持两个场景，情境感知型对话机器人在人机似度、流畅性和互动性上均显著优于静态节奏对照组，促进更深自我披露并提升参与度。在职业支持场景中，其被感知的倾听质量与情感信任度也更高。结果表明，将人类对话中的情境化节奏机制融入对话系统，可显著增强人机交互的共情能力。
原文摘要 · Abstract (English)
In human conversation, empathic dialogue requires nuanced temporal cues indicating whether the conversational partner is paying attention. This type of "active listening" is overlooked in the design of Conversational Agents (CAs), which use the same pacing for one conversation. To model the temporal cues in human conversation, we need CAs that dynamically adjust response pacing according to user input. We qualitatively analyzed ten cases of active listening to distill five context-aware pacing strategies: Reflective Silence, Facilitative Silence, Empathic Silence, Holding Space, and Immediate Response. In a between-subjects study (N=50) with two conversational scenarios (relationship and career-support), the context-aware agent scored higher than static-pacing control on perceived human-likeness, smoothness, and interactivity, supporting deeper self-disclosure and higher engagement. In the career support scenario, the CA yielded higher perceived listening quality and affective trust. This work shows how insights from human conversation like context-aware pacing can empower the design of more empathic human-AI communication.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。