11 papers
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…
Retrieval-Augmented LLM Agents: Learning to Learn from Experience
Thomas Palmeira Ferraz, Romain Deffayet, Vassilina Nikoulina +2
While large language models (LLMs) have advanced the development of general-purpose agents, achieving robust generalization to unseen tasks remains a significant challenge. Current…
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…
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…