Benoît Sagot

Explicit Learning and the LLM in Machine Translation

2025

This study explores an LLM's ability to learn new languages using explanations found in a grammar book-a process we term "explicit learning."To rigorously assess this ability, we design controlled translation experiments between English and constructed …

What Does BERT Learn about the Structure of Language?

2019

BERT is a recent language representation model that has surprisingly performed well in diverse language understanding benchmarks. This result indicates the possibility that BERT networks capture structural information about language. In this work, we provide …

CamemBERT: a Tasty French Language Model

2020

Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric de la Clergerie, Djamé Seddah, Benoît Sagot. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets

2022 · Transactions of the Association for Computational Linguistics

Abstract With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality …

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

2022 · arXiv (Cornell University)

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed …