用情感回应提升机器人倾听能力，让对话更深入有温度。
A Robot That Listens: Enhancing Self-Disclosure and Engagement Through Sentiment-based Backchannels and Active Listening
- 基于大模型实现情绪化反馈与主动倾听结合
- 实验显示用户自我披露量最高，感受被真正倾听
- 适合人机交互、情感计算方向研究者参考
随着社交机器人日益融入日常生活，它们需具备有意义对话及社会情感智能倾听能力。主动倾听与回应性反馈（backchanneling）或可增强机器人的沟通效果，促进更深层次的自我披露，带来共情感，并建立积极关系。为此，我们开发了一款基于大语言模型的社交机器人，能够根据语境生成情感适配的回应和主动倾听行为（主动倾听+回应），并对比其在激发用户自我披露方面的效果，分别与无倾听行为的对照组及仅有回应行为的机器人进行比较。通过65名参与者实验发现，与具备主动倾听的机器人交流时，用户感知互动更积极，自我披露程度最高，并报告了最强的被倾听感。结果表明，在社交机器人中引入主动倾听行为，有望改善人机沟通，助力构建更深层的人机关系与信任。
原文摘要 · Abstract (English)
As social robots get more deeply integrated intoour everyday lives, they will be expected to engage in meaningful conversations and exhibit socio-emotionally intelligent listening behaviors when interacting with people. Active listening and backchanneling could be one way to enhance robots' communicative capabilities and enhance their effectiveness in eliciting deeper self-disclosure, providing a sense of empathy,and forming positive rapport and relationships with people.Thus, we developed an LLM-powered social robot that can exhibit contextually appropriate sentiment-based backchannelingand active listening behaviors (active listening+backchanneling) and compared its efficacy in eliciting people's self-disclosurein comparison to robots that do not exhibit any of these listening behaviors (control) and a robot that only exhibitsbackchanneling behavior (backchanneling-only). Through ourexperimental study with sixty-five participants, we found theparticipants who conversed with the active listening robot per-ceived the interactions more positively, in which they exhibited the highest self-disclosures, and reported the strongest senseof being listened to. The results of our study suggest that the implementation of active listening behaviors in social robotshas the potential to improve human-robot communication andcould further contribute to the building of deeper human-robot relationships and rapport.
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