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.

通过 HealMe 模型，本文探讨了大型语言模型在认知重构中的作用，以及它在心理治疗中引起积极影响的有效性和潜在的正面影响，通过采用基于心理治疗框架的移情对话，指导客户从情况、观点和行动建议等方面进行认知重构，实现深层次负面思维的转变和理性维度的培养。

HealMe: 利用大型语言模型中的认知重构技术进行心理疗法

HealMe: Harnessing Cognitive Reframing in Large Language Models for  Psychotherapy

A proven therapeutic technique to overcome negative thoughts is to replace
them with a more hopeful "reframed thought." Although therapy can help people
practice and learn this Cognitive Reframing of Negative Thoughts, clinician
shortages and mental health stigma commonly limit people's access to therapy.
In this paper, we conduct a human-centered study of how language models may
assist people in reframing negative thoughts. Based on psychology literature,
we define a framework of seven linguistic attributes that can be used to
reframe a thought. We develop automated metrics to measure these attributes and
validate them with expert judgements from mental health practitioners. We
collect a dataset of 600 situations, thoughts and reframes from practitioners
and use it to train a retrieval-enhanced in-context learning model that
effectively generates reframed thoughts and controls their linguistic
attributes. To investigate what constitutes a "high-quality" reframe, we
conduct an IRB-approved randomized field study on a large mental health website
with over 2,000 participants. Amongst other findings, we show that people
prefer highly empathic or specific reframes, as opposed to reframes that are
overly positive. Our findings provide key implications for the use of LMs to
assist people in overcoming negative thoughts.

该论文研究了语言模型如何帮助人们重新构建消极的想法，通过开发自动化指标来度量语言属性并训练一个提高检索上下文学习模型的数据集，并确认高度共情或特定于个体患者的重构更受人们青睐，这些发现对于使用语言模型协助人们克服消极思维具有重要意义。