  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.

Computation and Language