我们是否像对治疗师一样对机器人说话，而它们是否相应地回应？AI情感支持中的语言对齐
Do We Talk to Robots Like Therapists, and Do They Respond Accordingly? Language Alignment in AI Emotional Support
AI总结：
本研究通过对比机器人与人类治疗师的对话数据，发现90.88%的机器人披露可映射到人类治疗聚类，表明两者在主题和语义上高度对齐，机器人有潜力辅助心理健康干预。
摘要：
随着对话代理越来越多地参与情感支持性对话，理解它们的交互与传统治疗环境中的交互有多接近变得重要。本研究调查了与机器人分享的担忧是否与人与人（H2H）治疗会话中分享的担忧一致，以及机器人的回应是否在语义上镜像了人类治疗师的回应。我们分析了两个数据集：一个是用户与专业治疗师之间的交互（Hugging Face的NLP心理健康对话），另一个是涉及由大型语言模型（LLM，GPT-3.5）驱动的社交机器人（来自LuxAI的QTrobot）的支持性对话。使用句子嵌入和K-means聚类，我们通过应用基于距离的聚类拟合方法评估了跨代理主题对齐，该方法评估来自一种代理类型的回应是否可以映射到从另一种代理类型派生的聚类，并使用欧几里得距离进行验证。结果显示，90.88%的机器人对话披露可以映射到人类治疗数据集的聚类中，表明共享的主题结构。对于匹配的聚类，我们使用Transformer、Word2Vec和BERT嵌入比较了受试者以及治疗师和机器人的回应，揭示了两个数据集中受试者披露的强烈语义重叠，以及在不同代理类型（机器人vs.人类治疗师）对类似人类披露主题的回应中的强烈语义重叠。这些发现突显了机器人主导的支持性对话的相似性和边界，以及它们增强心理健康干预的潜力。
英文摘要：
As conversational agents increasingly engage in emotionally supportive dialogue, it is important to understand how closely their interactions resemble those in traditional therapy settings. This study investigates whether the concerns shared with a robot align with those shared in human-to-human (H2H) therapy sessions, and whether robot responses semantically mirror those of human therapists. We analyzed two datasets: one of interactions between users and professional therapists (Hugging Face's NLP Mental Health Conversations), and another involving supportive conversations with a social robot (QTrobot from LuxAI) powered by a large language model (LLM, GPT-3.5). Using sentence embeddings and K-means clustering, we assessed cross-agent thematic alignment by applying a distance-based cluster-fitting method that evaluates whether responses from one agent type map to clusters derived from the other, and validated it using Euclidean distances. Results showed that 90.88% of robot conversation disclosures could be mapped to clusters from the human therapy dataset, suggesting shared topical structure. For matched clusters, we compared the subjects as well as therapist and robot responses using Transformer, Word2Vec, and BERT embeddings, revealing strong semantic overlap in subjects' disclosures in both datasets, as well as in the responses given to similar human disclosure themes across agent types (robot vs. human therapist). These findings highlight both the parallels and boundaries of robot-led support conversations and their potential for augmenting mental health interventions.
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。
