Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning

• URL: http://arxiv.org/abs/2502.06873v1
• Date: Sat, 08 Feb 2025 07:32:48 GMT
• Title: Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning
• Authors: Subin Kim, Hoonrae Kim, Heejin Do, Gary Geunbae Lee,
• Abstract summary: We present a new dataset called Multi Modal-Cognitive Support Conversation (M2CoSC)<n>It pairs each GPT-4-generated dialogue with an image that reflects the virtual client's facial expressions.<n>To better mirror real psychotherapy, where facial expressions lead to interpreting implicit emotional evidence, we propose a multi-hop psychotherapeutic reasoning approach.
• Score: 6.468510459310326
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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