15 citations · 52 across the 32 of their papers we have counts for
23 papers · 1 filter
Inference-Free Multimodal Learned Sparse Retrieval for Production-Scale Visual Document Search
Gyu-Hwung Cho, Youngjune Lee, Kiyoon Jeong +5
As large-scale visual-document corpora such as arXiv papers and enterprise PDFs continue to grow, visual-document retrieval has gained increasing attention; yet it still lacks a de…
Efficient Listwise Reranking with Compressed Document Representations
Hervé Déjean, Stéphane Clinchant
Reranking, the process of refining the output from a first-stage retriever, is often considered computationally expensive, especially when using Large Language Models (LLMs). A com…
On the Challenges and Opportunities of Learned Sparse Retrieval for Code
Simon Lupart, Maxime Louis, Thibault Formal +2
Retrieval over large codebases is a key component of modern LLM-based software engineering systems. Existing approaches predominantly rely on dense embedding models, while learned…
Naver Labs Europe @ WSDM CUP | Multilingual Retrieval
Thibault Formal, Maxime Louis, Hervé Déjean +1
This report presents our participation to the WSDM Cup 2026 shared task on multilingual document retrieval from English queries. The task provides a challenging benchmark for cross…
Reranking with Compressed Document Representation
Hervé Déjean, Stéphane Clinchant
Reranking, the process of refining the output of a first-stage retriever, is often considered computationally expensive, especially with Large Language Models. Borrowing from recen…
OSCAR: Online Soft Compression And Reranking
Maxime Louis, Thibault Formal, Hervé Dejean +1
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge, leading to improved accuracy and relevance. However, scaling RAG pipel…