用于有效筛查抑郁症和焦虑症的增强型大型语言模型
Enhanced Large Language Models for Effective Screening of Depression and Anxiety
AI总结：
本文提出临床访谈合成流水线PsyInterview及LLM筛查系统EmoScan，能区分粗细粒度情绪障碍并开展高质量访谈，在筛查性能、解释生成与访谈技能上均超越基线及GPT-4，凸显了可扩展数据生成流水线对开发心理健康工具的重要性。
摘要：
抑郁症和焦虑症十分普遍，需要及时识别和管理。大型语言模型（LLMs）的最新进展提供了潜在的解决方案，但训练数据的高成本和伦理问题仍是挑战。本文提出了一条合成临床访谈的流水线，生成了1,157个交互式对话（PsyInterview），并提出了基于LLM的情绪障碍筛查系统EmoScan。EmoScan区分粗粒度（如焦虑症或抑郁症）和细粒度障碍（如重度抑郁症），并进行高质量访谈。评估表明，EmoScan在筛查情绪障碍方面超越了基础模型和其他LLM（如GPT-4）（F1-score=0.7467）。它还提供了更优的解释（BERTScore=0.9408），并展现出强大的泛化能力（在外部数据集上F1-score为0.67）。此外，经自动评分和人工评估验证，EmoScan在访谈技能上也优于基线。这项工作强调了可扩展的数据生成流水线对于开发有效心理健康LLM工具的重要性。
英文摘要：
Depressive and anxiety disorders are widespread, necessitating timely identification and management. Recent advances in Large Language Models (LLMs) offer potential solutions, yet high costs and ethical concerns about training data remain challenges. This paper introduces a pipeline for synthesizing clinical interviews, resulting in 1,157 interactive dialogues (PsyInterview), and presents EmoScan, an LLM-based emotional disorder screening system. EmoScan distinguishes between coarse (e.g., anxiety or depressive disorders) and fine disorders (e.g., major depressive disorders) and conducts high-quality interviews. Evaluations showed that EmoScan exceeded the performance of base models and other LLMs like GPT-4 in screening emotional disorders (F1-score=0.7467). It also delivers superior explanations (BERTScore=0.9408) and demonstrates robust generalizability (F1-score of 0.67 on an external dataset). Furthermore, EmoScan outperforms baselines in interviewing skills, as validated by automated ratings and human evaluations. This work highlights the importance of scalable data-generative pipelines for developing effective mental health LLM tools.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
