A Novel Nuanced Conversation Evaluation Framework for Large Language
  Models in Mental Health
Alexander Marrapese Basem Suleiman Imdad Ullah Juno Kim
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.