15 citations · 48 across the 19 of their papers we have counts for
4 papers · 1 filter
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…
Learning Retrieval Models with Sparse Autoencoders
Thibault Formal, Maxime Louis, Hervé Dejean +1
Sparse autoencoders (SAEs) provide a powerful mechanism for decomposing the dense representations produced by Large Language Models (LLMs) into interpretable latent features. We po…
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…
XProvence: Zero-Cost Multilingual Context Pruning for Retrieval-Augmented Generation
Youssef Mohamed, Mohamed Elhoseiny, Thibault Formal +1
This paper introduces XProvence, a multilingual zero-cost context pruning model for retrieval-augmented generation (RAG), trained on 16 languages and supporting 100+ languages thro…