Summary of Cbt-bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy, by Mian Zhang et al.
CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy
by Mian Zhang, Xianjun Yang, Xinlu Zhang, Travis Labrum, Jamie C. Chiu, Shaun M. Eack, Fei Fang, William Yang Wang, Zhiyu Zoey Chen
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper aims to explore the potential of Large Language Models (LLMs) in assisting professional psychotherapy. To evaluate this, a new benchmark, CBT-BENCH, is developed for assessing the systematic evaluation of cognitive behavioral therapy (CBT) assistance. The benchmark consists of three levels of tasks: basic knowledge acquisition, understanding cognitive models, and generating therapeutic responses. Representative LLMs are evaluated on this benchmark, showing that while they perform well in reciting CBT knowledge, they struggle with complex real-world scenarios requiring deep analysis and effective response generation. This highlights the need for further research to leverage AI assistance in psychotherapy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can help professionals in mental health therapy by providing important information and assisting conversations. The paper looks at how well these models do this by creating a new way to test them, called CBT-BENCH. There are three levels of tasks: learning basic facts about CBT, understanding complex concepts, and generating helpful responses. The results show that the models can share knowledge easily but struggle with more difficult conversations that require deep thinking. |
