MIRROR：多模态认知重构疗法用于应对阻抗
MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance
AI总结：
本文提出多模态方法MIRROR，利用面部图像合成数据集训练视觉语言模型，以应对心理治疗中的来访者阻抗，增强治疗联盟，优于文本CBT方法。
摘要：
近期研究探索了大型语言模型（LLMs）在心理治疗中的应用；然而，基于文本的认知行为疗法（CBT）模型往往难以应对来访者的阻抗，这可能削弱治疗联盟。为解决这一问题，我们提出了一种融合非语言线索的多模态方法，使AI治疗师能够更好地使其回应与来访者的消极情绪状态保持一致。具体而言，我们引入了一个新的合成数据集Mirror（多模态交互式阻抗应对），该数据集将每位来访者的陈述与相应的面部图像配对。利用该数据集，我们训练基线视觉语言模型（VLMs），使其能够分析面部线索、推断情绪，并生成共情回应以有效管理来访者的阻抗。随后，我们从治疗师的咨询技能以及在来访者阻抗情境下的治疗联盟强度两方面对这些模型进行评估。我们的结果表明，Mirror显著增强了AI治疗师应对阻抗的能力，优于现有的基于文本的CBT方法。人类专家评估进一步证实了我们的方法在管理来访者阻抗和促进治疗联盟方面的有效性。
英文摘要：
Recent studies have explored the use of large language models (LLMs) in psychotherapy; however, text-based cognitive behavioral therapy (CBT) models often struggle with client resistance, which can weaken therapeutic alliance. To address this, we propose a multimodal approach that incorporates nonverbal cues, which allows the AI therapist to better align its responses with the client's negative emotional state. Specifically, we introduce a new synthetic dataset, Mirror (Multimodal Interactive Rolling with Resistance), which is a novel synthetic dataset that pairs each client's statements with corresponding facial images. Using this dataset, we train baseline vision language models (VLMs) so that they can analyze facial cues, infer emotions, and generate empathetic responses to effectively manage client resistance. These models are then evaluated in terms of both their counseling skills as a therapist, and the strength of therapeutic alliance in the presence of client resistance. Our results demonstrate that Mirror significantly enhances the AI therapist's ability to handle resistance, which outperforms existing text-based CBT approaches. Human expert evaluations further confirm the effectiveness of our approach in managing client resistance and fostering therapeutic alliance.
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。
