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

arXiv 2408.16276 ↗First posted 29 Aug 2024cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 22 references that are in the map. First posted on arXiv 29 Aug 2024.

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References · 20

  1. 2023mPLUG-Owl: Modularization Empowers Large Language Models with Multimodalitycited by 1,038
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  10. 2024LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Reportcited by 47
  11. 2022ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classificationcited by 45
  12. 2021EventBERT: A Pre-Trained Model for Event Correlation Reasoningcited by 40
8 more
  1. 2023Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Modelscited by 36
  2. 2024Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizerscited by 33
  3. 2022Fine-Grained Distillation for Long Document Retrievalcited by 29
  4. 2023The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRscited by 29
  5. 2023Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using PsychoBenchcited by 28
  6. 2024MemoryMamba: Memory-Augmented State Space Model for Defect Recognitioncited by 24
  7. 2023Improving Cross-modal Alignment for Text-Guided Image Inpaintingcited by 21
  8. 2023K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learningcited by 3

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