1 citations · 2 across the 25 of their papers we have counts for
14 papers · 1 filter
MARQUIS: A Three-Stage Pipeline for Video Retrieval-Augmented Generation
Debashish Chakraborty, Dengjia Zhang, Jialiang Jin +7
Retrieval-augmented generation from videos requires systems to retrieve relevant audiovisual evidence from large corpora and synthesize it into coherent, attributed text. Current a…
A Replicability Study of XTR
Rohan Jha, Reno Kriz, Benjamin Van Durme
The XTR (conteXtual Token Retrieval) algorithm is a modification to ColBERT retrieval that avoids the costly step of fully gathering and reranking the candidates' embeddings by imp…
A Brief Comparison of Training-Free Multi-Vector Sequence Compression Methods
Rohan Jha, Chunsheng Zuo, Reno Kriz +1
While multi-vector retrieval models outperform single-vector models of comparable size in retrieval quality, their practicality is limited by substantially larger index sizes, driv…
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…
Multi-Vector Index Compression in Any Modality
Hanxiang Qin, Alexander Martin, Rohan Jha +3
We study efficient multi-vector retrieval for late interaction in any modality. Late interaction has emerged as a dominant paradigm for information retrieval in text, images, visua…
RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval
Tyler Skow, Alexander Martin, Benjamin Van Durme +2
Reranking is a critical component of modern retrieval systems, which typically pair an efficient first-stage retriever with a more expressive model to refine results. While large r…