3 papers
cs.IR2026
More Efficient LLM Reranking with Whole-Pool, Setwise, Long-Context Language Models
Hang Li, Chuting Yu, Teerapong Leelanupab +2
LLM-based re-rankers produce rankings through repeated local comparisons (listwise, pairwise or pointwise), requiring many sequential model calls. We study how long-context LLMs ca…
cs.IR2026
When LLM Judges Inflate Scores: Exploring Overrating in Relevance Assessment
Chuting Yu, Hang Li, Guido Zuccon +2
Human relevance assessment is time-consuming and cognitively intensive, limiting the scalability of Information Retrieval evaluation. This has led to growing interest in using larg…
cs.IR2024
TPRF: A Transformer-based Pseudo-Relevance Feedback Model for Efficient and Effective Retrieval
Hang Li, Chuting Yu, Ahmed Mourad +2
This paper considers Pseudo-Relevance Feedback (PRF) methods for dense retrievers in a resource constrained environment such as that of cheap cloud instances or embedded systems (e…