基于多跳心理治疗推理的多模态认知重构疗法
Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning
AI总结：
针对现有LLMs认知重构疗法忽视非语言证据的问题，该研究扩展至多模态，构建M2CoSC数据集并提出多跳心理治疗推理方法，提升VLMs治疗表现及建议的思考性与共情性。
摘要：
先前研究揭示了大型语言模型（LLMs）在支持认知重构疗法方面的潜力，但它们主要聚焦于基于文本的方法，常忽视现实治疗中至关重要的非语言证据的重要性。为填补这一空白，我们将文本认知重构扩展至多模态，纳入视觉线索。具体而言，我们提出了一个名为多模态认知支持对话（M2CoSC）的新数据集，该数据集将每个GPT-4生成的对话与反映虚拟来访者面部表情的图像配对。为更好地模拟现实心理治疗中面部表情引导解读隐含情绪证据的过程，我们提出一种多跳心理治疗推理方法，明确识别并纳入微妙证据。我们对LLMs和视觉语言模型（VLMs）的综合实验表明，M2CoSC数据集显著提升了VLMs作为心理治疗师的表现。此外，多跳心理治疗推理方法使VLMs能提供更具思考性和共情性的建议，优于标准提示方法。
英文摘要：
Previous research has revealed the potential of large language models (LLMs) to support cognitive reframing therapy; however, their focus was primarily on text-based methods, often overlooking the importance of non-verbal evidence crucial in real-life therapy. To alleviate this gap, we extend the textual cognitive reframing to multimodality, incorporating visual clues. Specifically, we present a new dataset called Multi Modal-Cognitive Support Conversation (M2CoSC), which pairs each GPT-4-generated dialogue with an image that reflects the virtual client's facial expressions. To better mirror real psychotherapy, where facial expressions lead to interpreting implicit emotional evidence, we propose a multi-hop psychotherapeutic reasoning approach that explicitly identifies and incorporates subtle evidence. Our comprehensive experiments with both LLMs and vision-language models (VLMs) demonstrate that the VLMs' performance as psychotherapists is significantly improved with the M2CoSC dataset. Furthermore, the multi-hop psychotherapeutic reasoning method enables VLMs to provide more thoughtful and empathetic suggestions, outperforming standard prompting methods.
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。
