Summary of Optimizing Psychological Counseling with Instruction-tuned Large Language Models, by Wenjie Li et al.
Optimizing Psychological Counseling with Instruction-Tuned Large Language Models
by Wenjie Li, Tianyu Sun, Kun Qian, Wenhong Wang
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers apply large language models (LLMs) to psychological counseling, aiming to enhance the capacity of AI systems in providing empathetic and supportive responses. The authors introduce an instruction-tuning method that utilizes specialized prompts to refine LLMs’ performance in this domain. They develop a comprehensive dataset of counseling-specific prompts through feedback from professional counselors and conduct rigorous evaluations using both automatic metrics and human assessments. The results show that the instruction-tuned model outperforms baseline LLMs, suggesting its potential as a scalable and accessible tool for mental health support. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence (AI) to help people with mental health issues. Researchers took language models, which are good at understanding and generating human-like text, and trained them to provide emotional support and helpful responses in counseling situations. They created a special dataset of prompts that counselors can use to guide the AI’s responses and tested it against other language models. The results show that this approach works well and could be used to create an AI system that helps people with mental health issues. |
Keywords
» Artificial intelligence » Instruction tuning
