57 citations · 150 across the 37 of their papers we have counts for
32 papers · 1 filter
Explaining When PRF Fails: Participatory Auditing for Selective Query Expansion
Zeyan Liang, Graham McDonald, Iadh Ounis
Pseudo-Relevance Feedback (PRF) improves retrieval effectiveness on average, but harms a substantial fraction of queries through query drift, an asymmetry hidden by aggregate offli…
Interpretable Uncertainty for Adaptive Retrieval and Reasoning in Question Answering
Ritajit Dey, Iadh Ounis, Graham McDonald
Large language models (LLMs) achieve a strong performance in question answering (QA), but remain prone to hallucinations and suffer from limited transparency. Retrieval-augmented g…
URecJPQ: Memory-efficient Multimodal Recommendation Models through RecJPQ in Large-Scale Scenarios
Giuseppe Spillo, Zixuan Yi, Aleksandr Petrov +3
Training state-of-the-art recommendation models on large-scale industrial datasets can be a challenging task due to the high number of users and items which are typically represent…
Temporal Fact Conflicts in LLMs: Reproducibility Insights from Unifying DYNAMICQA and MULAN
Ritajit Dey, Iadh Ounis, Graham McDonald +1
Large Language Models (LLMs) often struggle with temporal fact conflicts due to outdated or evolving information in their training data. Two recent studies with accompanying datase…
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…
Document Similarity Enhanced IPS Estimation for Unbiased Learning to Rank
Zeyan Liang, Graham McDonald, Iadh Ounis
Learning to Rank (LTR) models learn from historical user interactions, such as user clicks. However, there is an inherent bias in the clicks of users due to position bias, i.e., us…