16 papers
Robustness of IR Models to Collection Growth
Emmanouil Georgios Lionis, Debasis Ganguly, Sean MacAvaney
Information Retrieval (IR) systems seek to identify relevant documents within a collection. In practical applications, collections are dynamic, with documents frequently added. We…
A Theoretical Framework for Risk Analysis of Stochastic Rankers
Debasis Ganguly
Different from deterministic rankers that seek to maximize relevance at top ranks, stochastic ranking policies instead estimate distributions over permutations, from which rankings…
Dynamic Ranked List Truncation for Reranking Pipelines via LLM-generated Reference-Documents
Nilanjan Sinhababu, Soumedhik Bharati, Debasis Ganguly +1
Large Language Models (LLM) have been widely used in reranking. Computational overhead and large context lengths remain a challenging issue for LLM rerankers. Efficient reranking u…
RAQG-QPP: Query Performance Prediction with Retrieved Query Variants and Retrieval Augmented Query Generation
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Query Performance Prediction (QPP) estimates the retrieval quality of ranking models without the use of any human-assessed relevance judgements, and finds applications in query-spe…
SuiteEval: Simplifying Retrieval Benchmarks
Andrew Parry, Debasis Ganguly, Sean MacAvaney
Information retrieval evaluation often suffers from fragmented practices -- varying dataset subsets, aggregation methods, and pipeline configurations -- that undermine reproducibil…
LURE-RAG: Lightweight Utility-driven Reranking for Efficient RAG
Manish Chandra, Debasis Ganguly, Iadh Ounis
Most conventional Retrieval-Augmented Generation (RAG) pipelines rely on relevance-based retrieval, which often misaligns with utility -- that is, whether the retrieved passages ac…