Javier Parapar

Retrieval-Augmented Generation (RAG) systems rely on retrieved documents being concatenated into a model's input context, making both document ordering and context size critical yet controversial design choices. Prior work reports position-based effects such as lost in the middle and related long...

High-quality relevance judgements over large query sets are essential for evaluating Information Retrieval (IR) systems, yet manual annotation remains costly and time-consuming. Large Language Models (LLMs) have recently shown promise as automatic relevance assessors, but their reliability is sti...

Depression places substantial pressure on mental health services, and many people describe their experiences outside clinical settings in high-volume user-generated text (e.g., online forums and social media). Automatically identifying clinical symptom evidence in such text can therefore compleme...

Evaluating query suggestion systems in a manner that reflects real-world query formulation remains a persistent challenge. Most offline methodologies adopt static assumptions, such as users accepting all or the top-$e$ suggestions, ignoring the inherently selective and intent-driven nature of int...