Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models

Rafael Souza, Jia-Hao Lim, Alexander Davis
8/29/2024
cs.CL

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Abstract

Psychological consultation is essential for improving mental health and well-being, yet challenges such as the shortage of qualified professionals and scalability issues limit its accessibility. To address these challenges, we explore the use of large language models (LLMs) like GPT-4 to augment psychological consultation services. Our approach introduces a novel layered prompting system that dynamically adapts to user input, enabling comprehensive and relevant information gathering. We also develop empathy-driven and scenario-based prompts to enhance the LLM's emotional intelligence and contextual understanding in therapeutic settings. We validated our approach through experiments using a newly collected dataset of psychological consultation dialogues, demonstrating significant improvements in response quality. The results highlight the potential of our prompt engineering techniques to enhance AI-driven psychological consultation, offering a scalable and accessible solution to meet the growing demand for mental health support.

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@article{souza2024enhancing,
  title  = {Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models},
  author = {Rafael Souza and Jia-Hao Lim and Alexander Davis},
  year   = {2024},
  eprint = {2408.16276},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.16276v1}
}

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