A Novel Nuanced Conversation Evaluation Framework for Large Language Models in Mental Health

TL;DR

This paper introduces a new evaluation framework with quantitative metrics for assessing the nuanced conversation abilities of large language models in mental health, emphasizing safety and effectiveness.

Contribution

It develops a transferable, literature-based framework and metrics for evaluating LLMs in mental health, applied to popular models like GPT and Llama, with a focus on safety-critical responses.

Findings

GPT4 Turbo closely resembles verified therapists in mental health conversations.

Performance varies across mental health topics, with high accuracy in Parenting and Relationships.

The framework can be applied to other domains beyond mental health.

Abstract

Understanding the conversation abilities of Large Language Models (LLMs) can help lead to its more cautious and appropriate deployment. This is especially important for safety-critical domains like mental health, where someone's life may depend on the exact wording of a response to an urgent question. In this paper, we propose a novel framework for evaluating the nuanced conversation abilities of LLMs. Within it, we develop a series of quantitative metrics developed from literature on using psychotherapy conversation analysis literature. While we ensure that our framework and metrics are transferable by researchers to relevant adjacent domains, we apply them to the mental health field. We use our framework to evaluate several popular frontier LLMs, including some GPT and Llama models, through a verified mental health dataset. Our results show that GPT4 Turbo can perform significantly more similarly to verified therapists than other selected LLMs. We conduct additional analysis to examine how LLM conversation performance varies across specific mental health topics. Our results indicate that GPT4 Turbo performs well in achieving high correlation with verified therapists in particular topics such as Parenting and Relationships. We believe our contributions will help researchers develop better LLMs that, in turn, will more positively support people's lives.