Optimizing Psychological Counseling with Instruction-Tuned Large Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917699484909568 |
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| author | Li, Wenjie Sun, Tianyu Qian, Kun Wang, Wenhong |
| author_facet | Li, Wenjie Sun, Tianyu Qian, Kun Wang, Wenhong |
| contents | The advent of large language models (LLMs) has significantly advanced various fields, including natural language processing and automated dialogue systems. This paper explores the application of LLMs in psychological counseling, addressing the increasing demand for mental health services. We present a method for instruction tuning LLMs with specialized prompts to enhance their performance in providing empathetic, relevant, and supportive responses. Our approach involves developing a comprehensive dataset of counseling-specific prompts, refining them through feedback from professional counselors, and conducting rigorous evaluations using both automatic metrics and human assessments. The results demonstrate that our instruction-tuned model outperforms several baseline LLMs, highlighting its potential as a scalable and accessible tool for mental health support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13617 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Psychological Counseling with Instruction-Tuned Large Language Models Li, Wenjie Sun, Tianyu Qian, Kun Wang, Wenhong Computation and Language Artificial Intelligence The advent of large language models (LLMs) has significantly advanced various fields, including natural language processing and automated dialogue systems. This paper explores the application of LLMs in psychological counseling, addressing the increasing demand for mental health services. We present a method for instruction tuning LLMs with specialized prompts to enhance their performance in providing empathetic, relevant, and supportive responses. Our approach involves developing a comprehensive dataset of counseling-specific prompts, refining them through feedback from professional counselors, and conducting rigorous evaluations using both automatic metrics and human assessments. The results demonstrate that our instruction-tuned model outperforms several baseline LLMs, highlighting its potential as a scalable and accessible tool for mental health support. |
| title | Optimizing Psychological Counseling with Instruction-Tuned Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.13617 |