Marcos Fernández-Pichel

Marcos Fernández-Pichel, Juan C. Pichel, David E. Losada

Search engines (SEs) have traditionally been primary tools for information seeking, but the new Large Language Models (LLMs) are emerging as powerful alternatives, particularly for question-answering tasks. This study compares the performance of four popular SEs, seven LLMs, and retrieval-augmented (RAG) variants in answering 150 health-related questions from the TREC Health Misinformation (HM) Track. Results reveal SEs correctly answer between 50 and 70% of questions, often hindered by many retrieval results not responding to the health question. LLMs deliver higher accuracy, correctly answering about 80% of questions, though their performance is sensitive to input prompts. RAG methods significantly enhance smaller LLMs' effectiveness, improving accuracy by up to 30% by integrating retrieval evidence.

Anxo Pérez, Marcos Fernández-Pichel, Javier Parapar et al.

Computational methods for depression detection aim to mine traces of depression from online publications posted by Internet users. However, solutions trained on existing collections exhibit limited generalisation and interpretability. To tackle these issues, recent studies have shown that identifying depressive symptoms can lead to more robust models. The eRisk initiative fosters research on this area and has recently proposed a new ranking task focused on developing search methods to find sentences related to depressive symptoms. This search challenge relies on the symptoms specified by the Beck Depression Inventory-II (BDI-II), a questionnaire widely used in clinical practice. Based on the participant systems' results, we present the DepreSym dataset, consisting of 21580 sentences annotated according to their relevance to the 21 BDI-II symptoms. The labelled sentences come from a pool of diverse ranking methods, and the final dataset serves as a valuable resource for advancing the development of models that incorporate depressive markers such as clinical symptoms. Due to the complex nature of this relevance annotation, we designed a robust assessment methodology carried out by three expert assessors (including an expert psychologist). Additionally, we explore here the feasibility of employing recent Large Language Models (ChatGPT and GPT4) as potential assessors in this complex task. We undertake a comprehensive examination of their performance, determine their main limitations and analyze their role as a complement or replacement for human annotators.

Pablo Saborido-Fernández, Marcos Fernández-Pichel, David E. Losada

Evaluating the capabilities of Large Language Models (LLMs) to assist teachers and students in educational tasks is receiving increasing attention. In this paper, we assess ChatGPT's capacities to solve and grade real programming exams, from an accredited BSc degree in Computer Science, written in Spanish. Our findings suggest that this AI model is only effective for solving simple coding tasks. Its proficiency in tackling complex problems or evaluating solutions authored by others are far from effective. As part of this research, we also release a new corpus of programming tasks and the corresponding prompts for solving the problems or grading the solutions. This resource can be further exploited by other research teams.