3 papers
cs.IR2026
Whole-Pool Setwise Reranking with Long-Context Language Models
Hang Li, Chuting Yu, Teerapong Leelanupab +2
Previous LLM-based passage re-rankers are often expensive and slow because the input context constraints require the LLM to make many dependent model calls. We study how recent lon…
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…