activity
20232026
most citedCopyright Traps for Large Language Models

24 citations · 59 across the 17 of their papers we have counts for

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Showing cs.CLShow all

8 papers · 1 filter

cs.CL2025

Should We Still Pretrain Encoders with Masked Language Modeling?

Hippolyte Gisserot-Boukhlef, Nicolas Boizard, Manuel Faysse +5

Learning high-quality text representations is fundamental to a wide range of NLP tasks. While encoder pretraining has traditionally relied on Masked Language Modeling (MLM), recent…

cs.CL2025

EuroLLM-9B: Technical Report

Pedro Henrique Martins, João Alves, Patrick Fernandes +14

This report presents EuroLLM-9B, a large language model trained from scratch to support the needs of European citizens by covering all 24 official European Union languages and 11 a…

cs.CL2025

EuroBERT: Scaling Multilingual Encoders for European Languages

Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Duarte M. Alves +16

General-purpose multilingual vector representations, used in retrieval, regression and classification, are traditionally obtained from bidirectional encoder models. Despite their w…

cs.CL2025★ 15 cited

MMTEB: Massive Multilingual Text Embedding Benchmark

Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83

Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…

cs.CL2024★ 3 cited

EuroLLM: Multilingual Language Models for Europe

Pedro Henrique Martins, Patrick Fernandes, João Alves +12

The quality of open-weight LLMs has seen significant improvement, yet they remain predominantly focused on English. In this paper, we introduce the EuroLLM project, aimed at develo…

cs.CL2024

SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)

Matthieu Meeus, Igor Shilov, Shubham Jain +3

Whether LLMs memorize their training data and what this means, from measuring privacy leakage to detecting copyright violations, has become a rapidly growing area of research. In t…

cs.CL2024★ 24 cited

Copyright Traps for Large Language Models

Matthieu Meeus, Igor Shilov, Manuel Faysse +1

Questions of fair use of copyright-protected content to train Large Language Models (LLMs) are being actively debated. Document-level inference has been proposed as a new task: inf…

cs.CL2024★ 8 cited

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…