研究大模型如何解释机器人在多人对话中的干预行为。
Understanding LLM Intervention Explanations in Multi-Party Human-Robot Interaction
- 用大模型生成机器人干预决策的文本解释。
- 610条解释中发现5个核心主题，强调协作与对话流畅性。
- 不同角色机器人干预逻辑不同，适合人机交互可解释性研究者。
大型语言模型（LLMs）正被越来越多地用于社交机器人，以支持自然的群体互动，但在复杂多参与者场景下的作用仍不明确。本文研究了一个基于大模型的协调系统在三人参与、两机器人协同的多主体互动中生成的干预解释。通过24组（共66名大学生）的对照实验，比较了同质条件（两机器人角色相同，均为“移动者”）与异质条件（两机器人角色不同，分别为“移动者”和“反对者”）。每轮对话中，大模型协调器决定是否干预，并生成解释文本。对610条干预解释进行主题分析，识别出五个重复出现的主题。结果显示，解释内容以促进交流为主，强调共识、参与度与互动流畅性；尽管模式整体稳定，但角色分化显现：移动者侧重协调，反对者更关注目标导向的干预。该研究为可解释AI提供了实时多主体人机交互中干预决策的解释机制参考。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly embedded in social robots to support natural group interactions, yet their role in complex multi-party settings remains underexplored. In particular, it is unclear how LLM-driven robots decide when and why to intervene in group conversations. This paper investigates the intervention explanations generated by an LLM-based orchestrator in a multi-party interaction involving three human participants and two robots. We conducted a between-subjects study with 24 groups (66 university students), comparing a homogeneous condition (two robots with the same role, i.e., a mover) and a heterogeneous condition (two robots with different roles, i.e., a mover and an opposer). At each conversational turn, the LLM orchestrator decided whether to intervene and generated a textual explanation of its decision. We performed a thematic analysis of 610 intervention explanations, identifying five recurring themes. Results show that explanations are facilitation-oriented, emphasizing agreement, participation, and interaction flow. While patterns remain stable across conditions, role differentiation emerges: the mover supports coordination, whereas the opposer drives goal-oriented interventions. These findings contribute to explainable AI by characterizing how LLM-driven systems justify intervention decisions in real-time, multi-party human-robot interaction.
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