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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.09426 |
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| _version_ | 1866913653284929536 |
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| author | Xu, Ancheng Yang, Di Li, Renhao Zhu, Jingwei Tan, Minghuan Yang, Min Qiu, Wanxin Ma, Mingchen Wu, Haihong Li, Bingyu Sha, Feng Li, Chengming Hu, Xiping Qu, Qiang Wong, Derek F. Xu, Ruifeng |
| author_facet | Xu, Ancheng Yang, Di Li, Renhao Zhu, Jingwei Tan, Minghuan Yang, Min Qiu, Wanxin Ma, Mingchen Wu, Haihong Li, Bingyu Sha, Feng Li, Chengming Hu, Xiping Qu, Qiang Wong, Derek F. Xu, Ruifeng |
| contents | Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues, while online automated counseling offers a potential solution for those hesitant to seek help due to feelings of shame. Cognitive Behavioral Therapy (CBT) is an essential and widely used approach in psychological counseling. The advent of large language models (LLMs) and agent technology enables automatic CBT diagnosis and treatment. However, current LLM-based CBT systems use agents with a fixed structure, limiting their self-optimization capabilities, or providing hollow, unhelpful suggestions due to redundant response patterns. In this work, we utilize Quora-like and YiXinLi single-round consultation models to build a general agent framework that generates high-quality responses for single-turn psychological consultation scenarios. We use a bilingual dataset to evaluate the quality of single-response consultations generated by each framework. Then, we incorporate dynamic routing and supervisory mechanisms inspired by real psychological counseling to construct a CBT-oriented autonomous multi-agent framework, demonstrating its general applicability. Experimental results indicate that AutoCBT can provide higher-quality automated psychological counseling services. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09426 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling Xu, Ancheng Yang, Di Li, Renhao Zhu, Jingwei Tan, Minghuan Yang, Min Qiu, Wanxin Ma, Mingchen Wu, Haihong Li, Bingyu Sha, Feng Li, Chengming Hu, Xiping Qu, Qiang Wong, Derek F. Xu, Ruifeng Computation and Language Traditional in-person psychological counseling remains primarily niche, often chosen by individuals with psychological issues, while online automated counseling offers a potential solution for those hesitant to seek help due to feelings of shame. Cognitive Behavioral Therapy (CBT) is an essential and widely used approach in psychological counseling. The advent of large language models (LLMs) and agent technology enables automatic CBT diagnosis and treatment. However, current LLM-based CBT systems use agents with a fixed structure, limiting their self-optimization capabilities, or providing hollow, unhelpful suggestions due to redundant response patterns. In this work, we utilize Quora-like and YiXinLi single-round consultation models to build a general agent framework that generates high-quality responses for single-turn psychological consultation scenarios. We use a bilingual dataset to evaluate the quality of single-response consultations generated by each framework. Then, we incorporate dynamic routing and supervisory mechanisms inspired by real psychological counseling to construct a CBT-oriented autonomous multi-agent framework, demonstrating its general applicability. Experimental results indicate that AutoCBT can provide higher-quality automated psychological counseling services. |
| title | AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.09426 |