A Computational Framework for Behavioral Assessment of LLM Therapists

• URL: http://arxiv.org/abs/2401.00820v2
• Date: Thu, 28 Nov 2024 22:01:57 GMT
• Title: A Computational Framework for Behavioral Assessment of LLM Therapists
• Authors: Yu Ying Chiu, Ashish Sharma, Inna Wanyin Lin, Tim Althoff,
• Abstract summary: Large language models (LLMs) like ChatGPT have increased interest in their use as therapists to address mental health challenges. We propose BOLT, a proof-of-concept computational framework to systematically assess the conversational behavior of LLM therapists.
• Score: 7.665475687919995
• License:
• Abstract: 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.

Related papers

• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment. We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews. Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy [67.23830698947637]
  We propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance. We include three levels of tasks in CBT-BENCH: I: Basic CBT knowledge acquisition, with the task of multiple-choice questions; II: Cognitive model understanding, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; III: Therapeutic response generation, with the task of generating responses to patient speech in CBT therapy sessions. Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios
  arXiv  Detail & Related papers  (2024-10-17T04:52:57Z)
• Therapy as an NLP Task: Psychologists' Comparison of LLMs and Human Peers in CBT [6.812247730094931]
  We investigate the potential and limitations of using large language models (LLMs) as providers of evidence-based therapy. We replicated publicly accessible mental health conversations rooted in Cognitive Behavioral Therapy (CBT) to compare session dynamics and counselor's CBT-based behaviors. Our findings show that the peer sessions are characterized by empathy, small talk, therapeutic alliance, and shared experiences but often exhibit therapist drift.
  arXiv  Detail & Related papers  (2024-09-03T19:19:13Z)
• Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions [12.455050661682051]
  We propose a framework that employs two large language models (LLMs) via role-playing for simulating counselor-client interactions. Our framework involves two LLMs, one acting as a client equipped with a specific and real-life user profile and the other playing the role of an experienced counselor.
  arXiv  Detail & Related papers  (2024-08-28T13:29:59Z)
• An Active Inference Strategy for Prompting Reliable Responses from Large Language Models in Medical Practice [0.0]
  Large Language Models (LLMs) are non-deterministic, may provide incorrect or harmful responses, and cannot be regulated to assure quality control. Our proposed framework refines LLM responses by restricting their primary knowledge base to domain-specific datasets containing validated medical information. We conducted a validation study where expert cognitive behaviour therapy for insomnia therapists evaluated responses from the LLM in a blind format.
  arXiv  Detail & Related papers  (2024-07-23T05:00:18Z)
• LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
  We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
  arXiv  Detail & Related papers  (2024-06-09T09:03:11Z)
• A Novel Nuanced Conversation Evaluation Framework for Large Language Models in Mental Health [42.711913023646915]
  We propose a novel framework for evaluating the nuanced conversation abilities of Large Language Models (LLMs) Within it, we develop a series of quantitative metrics developed from literature on using psychotherapy conversation analysis literature. We use our framework to evaluate several popular frontier LLMs, including some GPT and Llama models, through a verified mental health dataset.
  arXiv  Detail & Related papers  (2024-03-08T23:46:37Z)
• Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent Psychological Counseling [1.1780706927049207]
  This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents. LLMs present a novel opportunity to augment traditional psychological counseling methods.
  arXiv  Detail & Related papers  (2023-11-12T07:55:39Z)
• A Survey on Evaluation of Large Language Models [87.60417393701331]
  Large language models (LLMs) are gaining increasing popularity in both academia and industry. This paper focuses on three key dimensions: what to evaluate, where to evaluate, and how to evaluate.
  arXiv  Detail & Related papers  (2023-07-06T16:28:35Z)
• Revisiting the Reliability of Psychological Scales on Large Language Models [62.57981196992073]
  This study aims to determine the reliability of applying personality assessments to Large Language Models. Analysis of 2,500 settings per model, including GPT-3.5, GPT-4, Gemini-Pro, and LLaMA-3.1, reveals that various LLMs show consistency in responses to the Big Five Inventory.
  arXiv  Detail & Related papers  (2023-05-31T15:03:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.