In this work, we present the contribution of the BLUE team in the eRisk Lab task on searching for symptoms of depression. The task consists of retrieving and ranking Reddit social media sentences that convey symptoms of depression from the BDI-II questionnaire. Given that synthetic data provided by LLMs have been proven to be a reliable method for augmenting data and fine-tuning downstream models, we chose to generate synthetic data using ChatGPT for each of the symptoms of the BDI-II questionnaire. We designed a prompt such that the generated data contains more richness and semantic diversity than the BDI-II responses for each question and, at the same time, contains emotional and anecdotal experiences that are specific to the more intimate way of sharing experiences on Reddit. We perform semantic search and rank the sentences' relevance to the BDI-II symptoms by cosine similarity. We used two state-of-the-art transformer-based models (MentalRoBERTa and a variant of MPNet) for embedding the social media posts, the original and generated responses of the BDI-II. Our results show that using sentence embeddings from a model designed for semantic search outperforms the approach using embeddings from a model pre-trained on mental health data. Furthermore, the generated synthetic data were proved too specific for this task, the approach simply relying on the BDI-II responses had the best performance.

翻译：本文介绍了BLUE团队在eRisk Lab任务中的贡献，该任务旨在搜索抑郁症状。任务要求从Reddit社交媒体句子中检索并排序符合BDI-II问卷中抑郁症状的语句。鉴于大型语言模型生成的合成数据已被证明是数据增强和下游模型微调的可靠方法，我们选择利用ChatGPT为BDI-II问卷中的每种症状生成合成数据。我们设计了一种提示词，使生成的数据比BDI-II每个问题的回答更丰富且语义更多样化，同时包含特定于Reddit上更私密分享方式的情绪化和轶事性体验。我们通过余弦相似度进行语义搜索并对句子与BDI-II症状的相关性进行排序。我们使用了两种基于Transformer的最先进模型（MentalRoBERTa和MPNet变体）来嵌入社交媒体帖子、BDI-II原始回答及生成回答。结果表明，使用专为语义搜索设计的模型生成的句子嵌入，其性能优于使用在心理健康数据上预训练的模型生成的嵌入。此外，生成的合成数据被证明对此任务过于具体，而仅依赖BDI-II回答的方法取得了最佳性能。