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arXiv · 2502.06873

Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning

Subin Kim·Hoonrae Kim·Heejin Do·Gary Geunbae Lee
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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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Subin Kim, Hoonrae Kim, Heejin Do, Gary Geunbae Lee. 2025-02-08. Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning. https://arxiv.org/abs/2502.06873

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