1 citations · 2 across the 12 of their papers we have counts for
8 papers · 1 filter
ColBERTSaR: Sparsified ColBERT Index via Product Quantization
Eugene Yang, Andrew Yates, Dawn Lawrie +3
While ColBERT is an effective neural retrieval architecture, it requires a heavy index structure to support candidate set retrieval based on approximated token embeddings, gatherin…
CoverageBench: Evaluating Information Coverage across Tasks and Domains
Saron Samuel, Andrew Yates, Dawn Lawrie +4
We wish to measure the information coverage of an ad hoc retrieval algorithm, that is, how much of the range of available relevant information is covered by the search results. Inf…
Does Reasoning Make Search More Fair? Comparing Fairness in Reasoning and Non-Reasoning Rerankers
Saron Samuel, Benjamin Van Durme, Eugene Yang
While reasoning rerankers, such as Rank1, have demonstrated strong abilities in improving ranking relevance, it is unclear how they perform on other retrieval qualities such as fai…
Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage
Saron Samuel, Alexander Martin, Eugene Yang +5
Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation. While the relat…
NeuCLIRTech: Chinese Monolingual and Cross-Language Information Retrieval Evaluation in a Challenging Domain
Dawn Lawrie, James Mayfield, Eugene Yang +6
Measuring advances in retrieval requires test collections with relevance judgments that can faithfully distinguish systems. This paper presents NeuCLIRTech, an evaluation collectio…
Overview of the TREC 2025 RAGTIME Track
Dawn Lawrie, Sean MacAvaney, James Mayfield +3
The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multilingual source documents. The track has cr…
LANCER: LLM Reranking for Nugget Coverage
Jia-Huei Ju, François G. Landry, Eugene Yang +2
Unlike short-form retrieval-augmented generation (RAG), such as factoid question answering, long-form RAG requires retrieval to provide documents covering a wide range of relevant…
RoutIR: Fast Serving of Retrieval Pipelines for Retrieval-Augmented Generation
Eugene Yang, Andrew Yates, Dawn Lawrie +2
Retrieval models are key components of Retrieval-Augmented Generation (RAG) systems, which generate search queries, process the documents returned, and generate a response. RAG sys…