28 citations · 129 across the 32 of their papers we have counts for
5 papers · 1 filter
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Orion Weller, Kyle Lo, David Wadden +4
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universal…
MegaWika: Millions of reports and their sources across 50 diverse languages
Samuel Barham, Orion Weller, Michelle Yuan +9
To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages,…
"According to ...": Prompting Language Models Improves Quoting from Pre-Training Data
Orion Weller, Marc Marone, Nathaniel Weir +3
Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data. Inspired by the journalistic device of "according to sources", we…
NevIR: Negation in Neural Information Retrieval
Orion Weller, Dawn Lawrie, Benjamin Van Durme
Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as t…
Synthetic Cross-language Information Retrieval Training Data
James Mayfield, Eugene Yang, Dawn Lawrie +5
A key stumbling block for neural cross-language information retrieval (CLIR) systems has been the paucity of training data. The appearance of the MS MARCO monolingual training set…