From the 1 of 7 linked papers with an AI index.
7 papers
User Preference Induction with LLMs for Offline Top-N Recommendation Evaluation
David Otero, Javier Parapar
The paper introduces a framework that uses large language models to create textual user profiles and to judge the relevance of items without existing feedback, thereby expanding of…
Hybrid Pooling with LLMs via Relevance Context Learning
David Otero, Javier Parapar
High-quality relevance judgements over large query sets are essential for evaluating Information Retrieval (IR) systems, yet manual annotation remains costly and time-consuming. La…
Learning Evidence of Depression Symptoms via Prompt Induction
Eliseo Bao, Anxo Perez, David Otero +1
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., onl…
LLM-Assisted Pseudo-Relevance Feedback
David Otero, Javier Parapar
Query expansion is a long-standing technique to mitigate vocabulary mismatch in ad hoc Information Retrieval. Pseudo-relevance feedback methods, such as RM3, estimate an expanded q…
Can LLMs Evaluate What They Cannot Annotate? Revisiting LLM Reliability in Hate Speech Detection
Paloma Piot, David Otero, Patricia MartÃn-Rodilla +1
Hate speech spreads widely online, harming individuals and communities, making automatic detection essential for large-scale moderation, yet detecting it remains difficult. Part of…
Limitations of Automatic Relevance Assessments with Large Language Models for Fair and Reliable Retrieval Evaluation
David Otero, Javier Parapar, Ãlvaro Barreiro
Offline evaluation of search systems depends on test collections. These benchmarks provide the researchers with a corpus of documents, topics and relevance judgements indicating wh…