---
title: "CBT-Bench — paper by Zhang et al. (2025) — summary and ideas"
description: "The authors worked with clinical psychologists, social work professors and hired… 90.7% accuracy. 30 references, cited by 11 papers here."
authors: ["Mian Zhang", "Xianjun Yang", "Xinlu Zhang", "Travis Labrum", "Jamie C. Chiu", "Shaun M. Eack", "Fei Fang", "William Yang Wang", "Zhiyu Chen"]
year: 2025
venue: "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.naacl-long.196/"
doi: "10.18653/v1/2025.naacl-long.196"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2025-cbt-bench-evaluating-large-language/"
---
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# CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy

**Authors:** [Mian Zhang](https://md.research.elamur.ai/free/en/authors/mian-zhang/), [Xianjun Yang](https://md.research.elamur.ai/free/en/authors/xianjun-yang/), [Xinlu Zhang](https://md.research.elamur.ai/free/en/authors/xinlu-zhang/), Travis Labrum, Jamie C. Chiu, [Shaun M. Eack](https://md.research.elamur.ai/free/en/authors/shaun-m-eack/), [Fei Fang](https://md.research.elamur.ai/free/en/authors/fei-fang/), [William Yang Wang](https://md.research.elamur.ai/free/en/authors/william-yang-wang/) and [Zhiyu Chen](https://md.research.elamur.ai/free/en/authors/zhiyu-chen/) · **Year:** 2025 · **Venue:** [Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2025](https://md.research.elamur.ai/free/en/venues/naacl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.naacl-long.196/) · [PDF](https://aclanthology.org/2025.naacl-long.196.pdf) · [DOI 10.18653/v1/2025.naacl-long.196](https://doi.org/10.18653/v1/2025.naacl-long.196) · [OpenAlex](https://openalex.org/W4411120632)

**Citations:** 12 citations (OpenAlex, 2026-10-14) · [11](https://md.research.elamur.ai/free/en/papers/zhang-2025-cbt-bench-evaluating-large-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Demand for mental health care far exceeds the number of available professionals. This paper asks how well large language models could support therapists practicing cognitive behavioral therapy (CBT). The authors worked with clinical psychologists, social work professors and hired CBT experts to build CBT-Bench, a benchmark of five new datasets arranged in three tiers of increasing difficulty:

1. Recalling CBT knowledge through multiple-choice questions.
2. Analysing a patient's cognitive model by classifying cognitive distortions and core beliefs at two levels of granularity.
3. Writing therapist-style replies to patient statements, using Deliberate Practice exercises as a privacy-friendly stand-in for real session transcripts.

Several popular LLMs were tested. Larger models answer knowledge questions well, but scaling brings little improvement in understanding patients' cognitive structures. All models struggle with fine-grained distortion and belief categories. In response generation, models tend to reason rigidly and challenge clients instead of respecting their autonomy and building rapport. These shortcomings are most visible when clients show resistance. The benchmark exposes concrete gaps that must be closed before AI can meaningfully assist psychotherapy.

## Key points
- **Level I (CBT-QA):** 220 test questions drawn from graduate CBT exams and expert-written items, with 50 more reserved for in-context examples. Human experts scored 90.7% accuracy.
- **Level II, three classification datasets:**
  - CBT-CD: 146 cognitive distortion examples.
  - CBT-PC: 184 examples across 3 primary core belief categories.
  - CBT-FC: 112 examples across 19 fine-grained core belief categories.
- **Level III (CBT-DP):** 156 Deliberate Practice exercises covering 10 aspects of CBT sessions at three difficulty levels. Model outputs were rated by experts against human reference responses.
- **Models tested:** six LLMs were evaluated on Levels I–II, including GPT-4o, Llama-3.1-8B and Llama-3.1-405B. All models performed worst on the "Responding to Client Resistance" exercise.
- **Availability and ethics:** the project was IRB-approved and used 8 US-based experts recruited via UpWork. Data and code are released on GitHub (mianzhang/CBT-Bench) for research use only.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Mian Zhang, Xianjun Yang, Xinlu Zhang, Travis Labrum, Jamie C. Chiu, Shaun M. Eack, Fei Fang, William Yang Wang and Zhiyu Chen. 2025. CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2025cbt,
  title = {{CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy}},
  author = {Mian Zhang, Xianjun Yang, Xinlu Zhang, Travis Labrum, Jamie C. Chiu, Shaun M. Eack, Fei Fang, William Yang Wang and Zhiyu Chen},
  booktitle = {Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.naacl-long.196},
  url = {https://doi.org/10.18653/v1/2025.naacl-long.196}
}
```

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*Updated 31 October 2026*