activity
20242026
most citedAn Interdisciplinary Approach to Human-Centered Machine Translation

2 citations · 2 across the 12 of their papers we have counts for

collaborators

19 papers

cs.CL2026

GlotOCR Bench: OCR Models Still Struggle Beyond a Handful of Unicode Scripts

Amir Hossein Kargaran, Nafiseh Nikeghbal, Jana Diesner +2

Optical character recognition (OCR) has advanced rapidly with the rise of vision-language models, yet evaluation has remained concentrated on a small cluster of high- and mid-resou…

cs.CL2026

EuroLLM-22B: Technical Report

Miguel Moura Ramos, Duarte M. Alves, Hippolyte Gisserot-Boukhlef +15

This report presents EuroLLM-22B, a large language model trained from scratch to support the needs of European citizens by covering all 24 official European Union languages and 11…

cs.CL2026

AdaptBPE: From General Purpose to Specialized Tokenizers

Vijini Liyanage, François Yvon

Subword tokenization methods, such as Byte-Pair Encoding (BPE), significantly impact the performance and efficiency of large language models (LLMs). The standard approach involves…

cs.CL2025

How Sampling Affects the Detectability of Machine-written texts: A Comprehensive Study

Matthieu Dubois, François Yvon, Pablo Piantanida

As texts generated by Large Language Models (LLMs) are ever more common and often indistinguishable from human-written content, research on automatic text detection has attracted g…

cs.CL2025

On the Entity-Level Alignment in Crosslingual Consistency

Yihong Liu, Mingyang Wang, François Yvon +1

Multilingual large language models (LLMs) are expected to recall factual knowledge consistently across languages. However, the factors that give rise to such crosslingual consisten…

cs.CL20252 cited

An Interdisciplinary Approach to Human-Centered Machine Translation

Marine Carpuat, Omri Asscher, Kalika Bali +17

Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between sy…