373 citations · 446 across the 24 of their papers we have counts for
21 papers · 1 filter
OmnilingualGAIA2: Evaluating the Multilingual Gap in Frontier AI Agents
Andrea Caciolai, Pere-Lluís Huguet Cabot, Chierh Cheng +11
Agentic benchmarks aim to measure how well AI agents plan, search, execute, and recover within realistic multi-tool environments, but they are almost exclusively in English. As AI…
Omnilingual SONAR: Cross-Lingual and Cross-Modal Sentence Embeddings Bridging Massively Multilingual Text and Speech
Omnilingual SONAR Team, João Maria Janeiro, Pere-Lluís Huguet Cabot +17
Cross-lingual sentence encoders typically cover only a few hundred languages and often trade downstream quality for stronger alignment, limiting their adoption. We introduce OmniSO…
Omnilingual MT: Machine Translation for 1,600 Languages
Omnilingual MT Team, Belen Alastruey, Niyati Bafna +29
High-quality machine translation (MT) can scale to hundreds of languages, setting a high bar for multilingual systems. However, compared to the world's 7,000 languages, current sys…
Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages
Omnilingual ASR team, Gil Keren, Artyom Kozhevnikov +30
Automatic speech recognition (ASR) has advanced in high-resource languages, but most of the world's 7,000+ languages remain unsupported, leaving thousands of long-tail languages be…
BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation
The Omnilingual MT Team, Pierre Andrews, Mikel Artetxe +14
BOUQuET is a multi-way, multicentric and multi-register/domain dataset and benchmark, and a broader collaborative initiative. This dataset is handcrafted in 8 non-English languages…
2M-BELEBELE: Highly Multilingual Speech and American Sign Language Comprehension Dataset
Marta R. Costa-jussà, Bokai Yu, Pierre Andrews +7
We introduce the first highly multilingual speech and American Sign Language (ASL) comprehension dataset by extending BELEBELE. Our dataset covers 74 spoken languages at the inters…
Large Concept Models: Language Modeling in a Sentence Representation Space
LCM team, Loïc Barrault, Paul-Ambroise Duquenne +18
LLMs have revolutionized the field of artificial intelligence and have emerged as the de-facto tool for many tasks. The current established technology of LLMs is to process input a…
Y-NQ: English-Yorùbá Evaluation dataset for Open-Book Reading Comprehension and Text Generation
Marta R. Costa-jussà, Joy Chen, Ifeoluwanimi Adebara +3
The purpose of this work is to share an English-Yorùbá evaluation dataset for open-book reading comprehension and text generation to assess the performance of models both in a high…
LCFO: Long Context and Long Form Output Dataset and Benchmarking
Marta R. Costa-jussà, Pierre Andrews, Mariano Coria Meglioli +10
This paper presents the Long Context and Form Output (LCFO) benchmark, a novel evaluation framework for assessing gradual summarization and summary expansion capabilities across di…
On the Role of Speech Data in Reducing Toxicity Detection Bias
Samuel J. Bell, Mariano Coria Meglioli, Megan Richards +6
Text toxicity detection systems exhibit significant biases, producing disproportionate rates of false positives on samples mentioning demographic groups. But what about toxicity de…
Linguini: A benchmark for language-agnostic linguistic reasoning
Eduardo Sánchez, Belen Alastruey, Christophe Ropers +3
We propose a new benchmark to measure a language model's linguistic reasoning skills without relying on pre-existing language-specific knowledge. The test covers 894 questions grou…
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
Haoran Sun, Renren Jin, Shaoyang Xu +10
Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource lan…
Towards Massive Multilingual Holistic Bias
Xiaoqing Ellen Tan, Prangthip Hansanti, Carleigh Wood +3
In the current landscape of automatic language generation, there is a need to understand, evaluate, and mitigate demographic biases as existing models are becoming increasingly mul…
Towards Red Teaming in Multimodal and Multilingual Translation
Christophe Ropers, David Dale, Prangthip Hansanti +9
Assessing performance in Natural Language Processing is becoming increasingly complex. One particular challenge is the potential for evaluation datasets to overlap with training da…
Seamless: Multilingual Expressive and Streaming Speech Translation
Seamless Communication, Loïc Barrault, Yu-An Chung +62
Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this wo…
The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages
Benjamin Muller, Belen Alastruey, Prangthip Hansanti +7
Gender biases in language generation systems are challenging to mitigate. One possible source for these biases is gender representation disparities in the training and evaluation d…
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
Seamless Communication, Loïc Barrault, Yu-An Chung +65
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed…
Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale
Marta R. Costa-jussà, Pierre Andrews, Eric Smith +6
We introduce a multilingual extension of the HOLISTICBIAS dataset, the largest English template-based taxonomy of textual people references: MULTILINGUALHOLISTICBIAS. This extensio…
HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation
David Dale, Elena Voita, Janice Lam +6
Hallucinations in machine translation are translations that contain information completely unrelated to the input. Omissions are translations that do not include some of the input…
Toxicity in Multilingual Machine Translation at Scale
Marta R. Costa-jussà, Eric Smith, Christophe Ropers +4
Machine Translation systems can produce different types of errors, some of which are characterized as critical or catastrophic due to the specific negative impact that they can hav…
No Language Left Behind: Scaling Human-Centered Machine Translation
NLLB Team, Marta R. Costa-jussà, James Cross +36
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However,…