对比三种LLM方法在心理健康文本分析中的表现，给出实用部署建议。
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG
- 比较提示工程、RAG与微调三种方法在心理健康分析中的效果。
- 微调准确率最高（情绪分类91%，心理状态检测80%），但需大量算力和数据。
- 提示工程和RAG灵活性高，适合资源有限场景，性能中等。
本研究系统比较了三种基于大语言模型（LLMs）进行心理健康文本分析的方法：提示工程、检索增强生成（RAG）和微调。基于LLaMA 3，在两个数据集上评估了这些方法在情绪分类和心理健康状况检测任务中的表现。微调在两项任务中均取得最高准确率（情绪分类91%，心理健康状况检测80%），但需要大量计算资源和大规模训练数据；而提示工程与RAG则展现出更高的部署灵活性，性能中等（准确率40%-68%）。研究结果为心理健康应用中基于LLM的解决方案实施提供了实践指导，揭示了准确率、计算开销与部署灵活性之间的权衡关系。
原文摘要 · Abstract (English)
This study presents a systematic comparison of three approaches for the analysis of mental health text using large language models (LLMs): prompt engineering, retrieval augmented generation (RAG), and fine-tuning. Using LLaMA 3, we evaluate these approaches on emotion classification and mental health condition detection tasks across two datasets. Fine-tuning achieves the highest accuracy (91% for emotion classification, 80% for mental health conditions) but requires substantial computational resources and large training sets, while prompt engineering and RAG offer more flexible deployment with moderate performance (40-68% accuracy). Our findings provide practical insights for implementing LLM-based solutions in mental health applications, highlighting the trade-offs between accuracy, computational requirements, and deployment flexibility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。