1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
Who Benefits from RAG? The Role of Exposure, Utility and Attribution Bias
Mahdi Dehghan, Graham McDonald
Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG) have achieved substantial improvements in accuracy by grounding their responses in external document…
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Large Language Models (LLMs) often struggle with temporal fact conflicts due to outdated or evolving information in their training data.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…
Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records
Erlend Frayling, Jake Lever, Graham McDonald
The challenge of accessing historical patient data for clinical research, while adhering to privacy regulations, is a significant obstacle in medical science. An innovative approac…