8 citations · 28 across the 11 of their papers we have counts for
12 papers
DAEDALUS: Bootstrapping Agent Memory from Self-Generated Tasks
Antoine Edy, Max Conti, Victor Xing +3
LLM agents often lack the operational knowledge to act reliably in new environments, as they must discover specific tool behaviors or environment conventions on their own. Without…
Experiential Reflective Learning for Self-Improving LLM Agents
Marc-Antoine Allard, Arnaud Teinturier, Victor Xing +1
Recent advances in large language models (LLMs) have enabled the development of autonomous agents capable of complex reasoning and multi-step problem solving. However, these agents…
ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios
António Loison, Quentin Macé, Antoine Edy +7
Retrieval-Augmented Generation (RAG) pipelines must address challenges beyond simple single-document retrieval, such as interpreting visual elements (tables, charts, images), synth…
ModernVBERT: Towards Smaller Visual Document Retrievers
Paul Teiletche, Quentin Macé, Max Conti +4
Retrieving specific information from a large corpus of documents is a prevalent industrial use case of modern AI, notably due to the popularity of Retrieval-Augmented Generation (R…
Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings
Max Conti, Manuel Faysse, Gautier Viaud +3
A limitation of modern document retrieval embedding methods is that they typically encode passages (chunks) from the same documents independently, often overlooking crucial context…
GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering
Sacha Muller, António Loison, Bilel Omrani +1
Retrieval-Augmented Generation (RAG) has emerged as a common paradigm to use Large Language Models (LLMs) alongside private and up-to-date knowledge bases. In this work, we address…
ColPali: Efficient Document Retrieval with Vision Language Models
Manuel Faysse, Hugues Sibille, Tony Wu +4
Documents are visually rich structures that convey information through text, but also figures, page layouts, tables, or even fonts. Since modern retrieval systems mainly rely on th…
CroissantLLM: A Truly Bilingual French-English Language Model
Manuel Faysse, Patrick Fernandes, Nuno M. Guerreiro +13
We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully op…
Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications
Manuel Faysse, Gautier Viaud, Céline Hudelot +1
Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the zero-shot capabilities of Large Language Models (LLMs), but in doing so induces new evaluation metric requ…
Improving Next-Application Prediction with Deep Personalized-Attention Neural Network
Jun Zhu, Gautier Viaud, Céline Hudelot
Recently, due to the ubiquity and supremacy of E-recruitment platforms, job recommender systems have been largely studied. In this paper, we tackle the next job application problem…
FQuAD2.0: French Question Answering and knowing that you know nothing
Quentin Heinrich, Gautier Viaud, Wacim Belblidia
Question Answering, including Reading Comprehension, is one of the NLP research areas that has seen significant scientific breakthroughs over the past few years, thanks to the conc…
Zolotarev Quadrature Rules and Load Balancing for the FEAST Eigensolver
Stefan Guettel, Eric Polizzi, Ping Tak Peter Tang +1
The FEAST method for solving large sparse eigenproblems is equivalent to subspace iteration with an approximate spectral projector and implicit orthogonalization. This relation all…