Optimizing Psychological Counseling with Instruction-Tuned Large Language Models

TL;DR

This paper introduces a method for instruction tuning large language models with counseling-specific prompts, significantly improving their ability to provide empathetic and relevant mental health support responses, demonstrating potential for scalable mental health assistance.

Contribution

It presents a novel instruction tuning approach with a specialized dataset and professional feedback to enhance LLMs for psychological counseling tasks.

Findings

Instruction-tuned LLMs outperform baseline models in counseling tasks

Enhanced empathetic and relevant responses in mental health support

Potential for scalable and accessible mental health assistance

Abstract

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.