From the 1 of 5 linked papers with an AI index.
5 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…
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…
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…
Towards Reliable Testing for Multiple Information Retrieval System Comparisons
David Otero, Javier Parapar, Ãlvaro Barreiro
Null Hypothesis Significance Testing is the \textit{de facto} tool for assessing effectiveness differences between Information Retrieval systems. Researchers use statistical tests…