toplogo
Sign In

Leveraging Large Language Models to Offer Targeted Cognitive Reappraisal for Emotional Support


Core Concepts
Large language models can be guided by expert-crafted constitutions to generate targeted cognitive reappraisals that help users reframe their negative appraisals of situations, which is an effective emotion regulation strategy.
Abstract
The content discusses the potential of using large language models (LLMs) to provide cognitive reappraisal, a psychological strategy for emotion regulation. It highlights that emotions are shaped by how individuals subjectively interpret or appraise their situations, and that changing these negative appraisals can lead to better emotional well-being. The authors present RESORT, a framework that consists of expert-crafted constitutions targeting six key cognitive appraisal dimensions. They evaluate the zero-shot capability of LLMs, guided by RESORT, to generate targeted reappraisals for emotional support. The evaluation involves: Individual guided reappraisal: LLMs generate reappraisals for each appraisal dimension separately. Iterative guided refinement: LLMs iteratively refine the reappraisal response across different dimensions. Incorporating explicit identification of appraisals before generating reappraisals. The authors conduct a first-of-its-kind expert evaluation by clinical psychologists, which shows that even 7B-scale LLMs guided by RESORT can generate reappraisals that significantly outperform human-written responses and non-appraisal-based prompting. The results provide strong evidence for using expert-informed constitutions to induce cognitive reappraisal capabilities in LLMs.
Stats
"There is nothing either good or bad, but thinking makes it so." "Emotions form a crucial aspect of people's well-being." "Compared to human peer-support providers, Large Language Models (LLMs) are indefatigable, have greater efficiency, are lower cost and more scalable." "Guided by RESORT, LLMs (even those at the 7B scale) produce cognitive reappraisals that significantly outperform human-written responses as well as non-appraisal-based prompting."
Quotes
"Cognitive appraisal theories of emotion assert that emotions stem from an individual's subjective understanding and interpretation of the situation." "Psychological research has consistently shown that reappraisal works both in producing short-term outcomes (e.g. more positive emotional states), but also long-term outcomes (better satisfaction with life, self-esteem, etc)." "Our work marks the first step towards inducing cognitive reappraisal capabilities from LLMs with psychologically-grounded frameworks."

Deeper Inquiries

How can the RESORT framework be extended to capture a broader range of cognitive appraisal dimensions and provide more comprehensive emotional support?

The RESORT framework can be extended by incorporating additional cognitive appraisal dimensions that are relevant to a wider range of emotional experiences. This can be achieved by consulting with a diverse group of psychologists and experts in various fields related to emotional well-being to identify and define new dimensions. By expanding the framework to include a more comprehensive set of dimensions, RESORT can offer more targeted and personalized cognitive reappraisals tailored to individual needs. Additionally, integrating machine learning algorithms to analyze and identify patterns in emotional responses can help in automatically adapting the framework to capture a broader spectrum of cognitive appraisals.

What are the potential risks and ethical considerations in deploying LLM-generated cognitive reappraisals at scale for emotional support, and how can they be mitigated?

Deploying LLM-generated cognitive reappraisals at scale for emotional support poses several risks and ethical considerations. One major concern is the potential for LLMs to provide inaccurate or harmful advice, leading to negative emotional outcomes for users. To mitigate this risk, thorough testing and validation of the LLM responses by experts in psychology and mental health should be conducted before deployment. Additionally, implementing strict guidelines and oversight mechanisms to monitor the quality and appropriateness of the responses generated by LLMs can help mitigate potential harm. Another ethical consideration is the privacy and confidentiality of user data shared during emotional support interactions. Ensuring robust data protection measures, such as encryption and anonymization of user information, is essential to safeguard user privacy. Transparency about the use of LLMs in providing emotional support and obtaining informed consent from users before engaging with the system are also crucial ethical practices to uphold.

How can the long-term impact of using guided cognitive reappraisals from LLMs on users' emotional well-being and mental health be empirically evaluated?

The long-term impact of using guided cognitive reappraisals from LLMs on users' emotional well-being and mental health can be empirically evaluated through longitudinal studies and controlled experiments. These studies can track the emotional well-being and mental health outcomes of users who receive cognitive reappraisals from LLMs over an extended period. Surveys, interviews, and standardized psychological assessments can be used to measure changes in emotional regulation, stress levels, and overall mental health. Additionally, qualitative research methods, such as in-depth interviews and focus groups, can provide insights into users' experiences with LLM-generated cognitive reappraisals and their perceived impact on their emotional well-being. Collaborating with mental health professionals to assess the effectiveness of LLM-generated cognitive reappraisals in supporting users' emotional needs can also provide valuable insights into the long-term benefits of this intervention. Overall, a comprehensive evaluation framework that combines quantitative and qualitative methods is essential to assess the sustained impact of guided cognitive reappraisals from LLMs on users' emotional well-being and mental health.
0