This article presents a method for prompt-based mental health screening from a large and noisy dataset of social media text. Our method uses GPT 3.5. prompting to distinguish publications that may be more relevant to the task, and then uses a straightforward bag-of-words text classifier to predict actual user labels. Results are found to be on pair with a BERT mixture of experts classifier, and incurring only a fraction of its computational costs.

翻译：本文提出一种从大规模且嘈杂的社交媒体文本数据中进行基于提示的心理健康筛查方法。该方法利用GPT 3.5提示技术筛选出可能更相关的内容，随后采用简单的词袋文本分类器预测用户真实标签。实验结果表明，该方法与基于BERT的混合专家分类器性能相当，但计算成本仅为其一小部分。