Assessing the Effectiveness of LLMs in Delivering Cognitive Behavioral Therapy
Navdeep Singh Bedi, Ana-Maria Bucur, Noriko Kando, Fabio Crestani · Mar 4, 2026 · Citations: 0
How to use this page
Provisional trustThis page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.
Best use
Background context only
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Evidence quality
Provisional
Derived from abstract and metadata only.
Abstract
As mental health issues continue to rise globally, there is an increasing demand for accessible and scalable therapeutic solutions. Many individuals currently seek support from Large Language Models (LLMs), even though these models have not been validated for use in counseling services. In this paper, we evaluate LLMs' ability to emulate professional therapists practicing Cognitive Behavioral Therapy (CBT). Using anonymized, transcribed role-play sessions between licensed therapists and clients, we compare two approaches: (1) a generation-only method and (2) a Retrieval-Augmented Generation (RAG) approach using CBT guidelines. We evaluate both proprietary and open-source models for linguistic quality, semantic coherence, and therapeutic fidelity using standard natural language generation (NLG) metrics, natural language inference (NLI), and automated scoring for skills assessment. Our results indicate that while LLMs can generate CBT-like dialogues, they are limited in their ability to convey empathy and maintain consistency.