HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866912204548210688 |
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| author | Xiao, Mengxi Xie, Qianqian Kuang, Ziyan Liu, Zhicheng Yang, Kailai Peng, Min Han, Weiguang Huang, Jimin |
| author_facet | Xiao, Mengxi Xie, Qianqian Kuang, Ziyan Liu, Zhicheng Yang, Kailai Peng, Min Han, Weiguang Huang, Jimin |
| contents | Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, and resource scarcity. Previous LLMs in cognitive reframing mainly converted negative emotions to positive ones, but these approaches have limited efficacy, often not promoting clients' self-discovery of alternative perspectives. In this paper, we unveil the Helping and Empowering through Adaptive Language in Mental Enhancement (HealMe) model. This novel cognitive reframing therapy method effectively addresses deep-rooted negative thoughts and fosters rational, balanced perspectives. Diverging from traditional LLM methods, HealMe employs empathetic dialogue based on psychotherapeutic frameworks. It systematically guides clients through distinguishing circumstances from feelings, brainstorming alternative viewpoints, and developing empathetic, actionable suggestions. Moreover, we adopt the first comprehensive and expertly crafted psychological evaluation metrics, specifically designed to rigorously assess the performance of cognitive reframing, in both AI-simulated dialogues and real-world therapeutic conversations. Experimental results show that our model outperforms others in terms of empathy, guidance, and logical coherence, demonstrating its effectiveness and potential positive impact on psychotherapy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05574 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy Xiao, Mengxi Xie, Qianqian Kuang, Ziyan Liu, Zhicheng Yang, Kailai Peng, Min Han, Weiguang Huang, Jimin Human-Computer Interaction Artificial Intelligence Computation and Language J.4 Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, and resource scarcity. Previous LLMs in cognitive reframing mainly converted negative emotions to positive ones, but these approaches have limited efficacy, often not promoting clients' self-discovery of alternative perspectives. In this paper, we unveil the Helping and Empowering through Adaptive Language in Mental Enhancement (HealMe) model. This novel cognitive reframing therapy method effectively addresses deep-rooted negative thoughts and fosters rational, balanced perspectives. Diverging from traditional LLM methods, HealMe employs empathetic dialogue based on psychotherapeutic frameworks. It systematically guides clients through distinguishing circumstances from feelings, brainstorming alternative viewpoints, and developing empathetic, actionable suggestions. Moreover, we adopt the first comprehensive and expertly crafted psychological evaluation metrics, specifically designed to rigorously assess the performance of cognitive reframing, in both AI-simulated dialogues and real-world therapeutic conversations. Experimental results show that our model outperforms others in terms of empathy, guidance, and logical coherence, demonstrating its effectiveness and potential positive impact on psychotherapy. |
| title | HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language J.4 |
| url | https://arxiv.org/abs/2403.05574 |