12 citations · 19 across the 19 of their papers we have counts for
8 papers · 1 filter
Predicting Partial Answer Quality and Utility in Agentic Retrieval-Augmented Generation
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Agentic Retrieval-Augmented Generation (RAG) has become a promising paradigm for multi-hop question answering, where a reasoning model iteratively issues queries to a retriever and…
Robustness of IR Models to Collection Growth
Emmanouil Georgios Lionis, Sean MacAvaney, Debasis Ganguly
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…
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…
Generate to Accelerate: Improved Reranking via LLM-Generated Pivot Documents
Nilanjan Sinhababu, Soumedhik Bharati, Debasis Ganguly +1
Common approaches to reduce the computational overhead of reranking models include identifying a candidate set of documents for reranking or constructing comparison graphs to minim…
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…
Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generation
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
The quality of answers generated by large language models (LLMs) in retrieval-augmented generation (RAG) is largely influenced by the contextual information contained in the retrie…