Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910748566880256 |
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| author | Sun, Xin Tang, Xiao Ali, Abdallah El Li, Zhuying Ren, Pengjie de Wit, Jan Pei, Jiahuan Bosch, Jos A. |
| author_facet | Sun, Xin Tang, Xiao Ali, Abdallah El Li, Zhuying Ren, Pengjie de Wit, Jan Pei, Jiahuan Bosch, Jos A. |
| contents | Recent advancements in large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, particularly in the context of motivational interviewing (MI). However, the inherent lack of transparency in LLM outputs presents significant challenges given the sensitive nature of psychotherapy. Applying MI strategies, a set of MI skills, to generate more controllable therapeutic-adherent conversations with explainability provides a possible solution. In this work, we explore the alignment of LLMs with MI strategies by first prompting the LLMs to predict the appropriate strategies as reasoning and then utilizing these strategies to guide the subsequent dialogue generation. We seek to investigate whether such alignment leads to more controllable and explainable generations. Multiple experiments including automatic and human evaluations are conducted to validate the effectiveness of MI strategies in aligning psychotherapy dialogue generation. Our findings demonstrate the potential of LLMs in producing strategically aligned dialogues and suggest directions for practical applications in psychotherapeutic settings. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_06527 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies Sun, Xin Tang, Xiao Ali, Abdallah El Li, Zhuying Ren, Pengjie de Wit, Jan Pei, Jiahuan Bosch, Jos A. Computation and Language Artificial Intelligence Recent advancements in large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, particularly in the context of motivational interviewing (MI). However, the inherent lack of transparency in LLM outputs presents significant challenges given the sensitive nature of psychotherapy. Applying MI strategies, a set of MI skills, to generate more controllable therapeutic-adherent conversations with explainability provides a possible solution. In this work, we explore the alignment of LLMs with MI strategies by first prompting the LLMs to predict the appropriate strategies as reasoning and then utilizing these strategies to guide the subsequent dialogue generation. We seek to investigate whether such alignment leads to more controllable and explainable generations. Multiple experiments including automatic and human evaluations are conducted to validate the effectiveness of MI strategies in aligning psychotherapy dialogue generation. Our findings demonstrate the potential of LLMs in producing strategically aligned dialogues and suggest directions for practical applications in psychotherapeutic settings. |
| title | Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2408.06527 |