  The CLEF eRisk Laboratory explores solutions to different tasks related to
risk detection on the Internet. In the 2023 edition, Task 1 consisted of
searching for symptoms of depression, the objective of which was to extract
user writings according to their relevance to the BDI Questionnaire symptoms.
Task 2 was related to the problem of early detection of pathological gambling
risks, where the participants had to detect users at risk as quickly as
possible. Finally, Task 3 consisted of estimating the severity levels of signs
of eating disorders. Our research group participated in the first two tasks,
proposing solutions based on Transformers. For Task 1, we applied different
approaches that can be interesting in information retrieval tasks. Two
proposals were based on the similarity of contextualized embedding vectors, and
the other one was based on prompting, an attractive current technique of
machine learning. For Task 2, we proposed three fine-tuned models followed by
decision policy according to criteria defined by an early detection framework.
One model presented extended vocabulary with important words to the addressed
domain. In the last task, we obtained good performances considering the
decision-based metrics, ranking-based metrics, and runtime. In this work, we
explore different ways to deploy the predictive potential of Transformers in
eRisk tasks.
