大模型在心理疾病检测中表现优于传统方法，尤其适合小样本和噪声数据。
Exploring Large Language Models for Detecting Mental Disorders
- 用大语言模型直接分析文本，无需复杂特征工程。
- 在小样本和杂乱文本上，大模型准确率显著更高。
- 临床确诊数据下，传统模型也能媲美大模型，适合精准医疗场景。
本文对比了传统机器学习、编码器型模型与大语言模型（LLMs）在抑郁和焦虑检测任务中的效果。研究使用五个俄语数据集，各数据集在格式和病理定义方式上存在差异。实验测试了基于语言特征的AutoML模型、多种BERT变体以及前沿的大语言模型作为病理分类器。结果表明，大语言模型在噪声大、样本少的数据集上表现更优，尤其当训练样本文本长度和文体差异显著时。然而，在来自临床确诊患者文本的训练数据上，心理语言学特征与编码器模型的表现可与大语言模型相当，凸显其在特定临床应用中的潜力。
原文摘要 · Abstract (English)
This paper compares the effectiveness of traditional machine learning methods, encoder-based models, and large language models (LLMs) on the task of detecting depression and anxiety. Five Russian-language datasets were considered, each differing in format and in the method used to define the target pathology class. We tested AutoML models based on linguistic features, several variations of encoder-based Transformers such as BERT, and state-of-the-art LLMs as pathology classification models. The results demonstrated that LLMs outperform traditional methods, particularly on noisy and small datasets where training examples vary significantly in text length and genre. However, psycholinguistic features and encoder-based models can achieve performance comparable to language models when trained on texts from individuals with clinically confirmed depression, highlighting their potential effectiveness in targeted clinical applications.
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