A Computational Framework for Behavioral Assessment of LLM Therapists
This addresses the need for systematic assessment of LLM therapists to ensure safe and effective mental health care, though it is incremental as a proof-of-concept framework.
The authors tackled the problem of evaluating LLM-based therapists by proposing BOLT, a computational framework that quantitatively measures their behavior across 13 psychotherapeutic approaches, finding that LLMs often resemble low-quality human therapy but reflect more on clients' needs and strengths.
The emergence of large language models (LLMs) like ChatGPT has increased interest in their use as therapists to address mental health challenges and the widespread lack of access to care. However, experts have emphasized the critical need for systematic evaluation of LLM-based mental health interventions to accurately assess their capabilities and limitations. Here, we propose BOLT, a proof-of-concept computational framework to systematically assess the conversational behavior of LLM therapists. We quantitatively measure LLM behavior across 13 psychotherapeutic approaches with in-context learning methods. Then, we compare the behavior of LLMs against high- and low-quality human therapy. Our analysis based on Motivational Interviewing therapy reveals that LLMs often resemble behaviors more commonly exhibited in low-quality therapy rather than high-quality therapy, such as offering a higher degree of problem-solving advice when clients share emotions. However, unlike low-quality therapy, LLMs reflect significantly more upon clients' needs and strengths. Our findings caution that LLM therapists still require further research for consistent, high-quality care.