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 for embedding the social media posts, the original and generated responses of the BDI-II, MentalRoBERTa and a variant of MPNet. Our results show that an approach using for sentence embeddings a model that is designed for semantic search outperforms the 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：在本工作中，我们介绍了BLUE团队在eRisk实验室任务中关于搜索抑郁症状的贡献。该任务包括从Reddit社交媒体句子中检索和排序与BDI-II问卷中抑郁症状相关的句子。鉴于大型语言模型生成的合成数据已被证明是增强数据和微调下游模型的可靠方法，我们选择使用ChatGPT为BDI-II问卷中的每种症状生成合成数据。我们设计了一个提示词，使得生成的数据比BDI-II问卷中每个问题的回答更丰富且具有语义多样性，同时包含特定于Reddit上更私密分享方式的情感和个人经历。我们通过余弦相似度执行语义搜索，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的最新模型来嵌入社交媒体帖子、BDI-II的原始和生成回答：MentalRoBERTa和MPNet的一个变体。我们的结果表明，使用为语义搜索设计的模型进行句子嵌入的方法优于在心理健康数据上预训练的模型。此外，生成的合成数据被证明对于该任务过于专一，仅依赖BDI-II回答的方法表现最佳。