@inproceedings{lee-etal-2024-cactus,
title = "Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory",
author = "Lee, Suyeon and
Kim, Sunghwan and
Kim, Minju and
Kang, Dongjin and
Yang, Dongil and
Kim, Harim and
Kang, Minseok and
Jung, Dayi and
Kim, Min Hee and
Lee, Seungbeen and
Chung, Kyong-Mee and
Yu, Youngjae and
Lee, Dongha and
Yeo, Jinyoung",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.832/",
doi = "10.18653/v1/2024.findings-emnlp.832",
pages = "14245--14274",
abstract = "Recently, the demand for psychological counseling has significantly increased as more individuals express concerns about their mental health. This surge has accelerated efforts to improve the accessibility of counseling by using large language models (LLMs) as counselors. To ensure client privacy, training open-source LLMs faces a key challenge: the absence of realistic counseling datasets. To address this, we introduce Cactus, a multi-turn dialogue dataset that emulates real-life interactions using the goal-oriented and structured approach of Cognitive Behavioral Therapy (CBT).We create a diverse and realistic dataset by designing clients with varied, specific personas, and having counselors systematically apply CBT techniques in their interactions. To assess the quality of our data, we benchmark against established psychological criteria used to evaluate real counseling sessions, ensuring alignment with expert evaluations.Experimental results demonstrate that Camel, a model trained with Cactus, outperforms other models in counseling skills, highlighting its effectiveness and potential as a counseling agent.We make our data, model, and code publicly available."
}
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<abstract>Recently, the demand for psychological counseling has significantly increased as more individuals express concerns about their mental health. This surge has accelerated efforts to improve the accessibility of counseling by using large language models (LLMs) as counselors. To ensure client privacy, training open-source LLMs faces a key challenge: the absence of realistic counseling datasets. To address this, we introduce Cactus, a multi-turn dialogue dataset that emulates real-life interactions using the goal-oriented and structured approach of Cognitive Behavioral Therapy (CBT).We create a diverse and realistic dataset by designing clients with varied, specific personas, and having counselors systematically apply CBT techniques in their interactions. To assess the quality of our data, we benchmark against established psychological criteria used to evaluate real counseling sessions, ensuring alignment with expert evaluations.Experimental results demonstrate that Camel, a model trained with Cactus, outperforms other models in counseling skills, highlighting its effectiveness and potential as a counseling agent.We make our data, model, and code publicly available.</abstract>
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%0 Conference Proceedings
%T Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory
%A Lee, Suyeon
%A Kim, Sunghwan
%A Kim, Minju
%A Kang, Dongjin
%A Yang, Dongil
%A Kim, Harim
%A Kang, Minseok
%A Jung, Dayi
%A Kim, Min Hee
%A Lee, Seungbeen
%A Chung, Kyong-Mee
%A Yu, Youngjae
%A Lee, Dongha
%A Yeo, Jinyoung
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F lee-etal-2024-cactus
%X Recently, the demand for psychological counseling has significantly increased as more individuals express concerns about their mental health. This surge has accelerated efforts to improve the accessibility of counseling by using large language models (LLMs) as counselors. To ensure client privacy, training open-source LLMs faces a key challenge: the absence of realistic counseling datasets. To address this, we introduce Cactus, a multi-turn dialogue dataset that emulates real-life interactions using the goal-oriented and structured approach of Cognitive Behavioral Therapy (CBT).We create a diverse and realistic dataset by designing clients with varied, specific personas, and having counselors systematically apply CBT techniques in their interactions. To assess the quality of our data, we benchmark against established psychological criteria used to evaluate real counseling sessions, ensuring alignment with expert evaluations.Experimental results demonstrate that Camel, a model trained with Cactus, outperforms other models in counseling skills, highlighting its effectiveness and potential as a counseling agent.We make our data, model, and code publicly available.
%R 10.18653/v1/2024.findings-emnlp.832
%U https://aclanthology.org/2024.findings-emnlp.832/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.832
%P 14245-14274
Markdown (Informal)
[Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory](https://aclanthology.org/2024.findings-emnlp.832/) (Lee et al., Findings 2024)
ACL
- Suyeon Lee, Sunghwan Kim, Minju Kim, Dongjin Kang, Dongil Yang, Harim Kim, Minseok Kang, Dayi Jung, Min Hee Kim, Seungbeen Lee, Kyong-Mee Chung, Youngjae Yu, Dongha Lee, and Jinyoung Yeo. 2024. Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 14245–14274, Miami, Florida, USA. Association for Computational Linguistics.