Omnilingual MT: Machine Translation for 1,600 Languages
Authors: name: Omnilingual MT Team, name: Belen Alastruey, name: Niyati Bafna, name: Andrea Caciolai, name: Kevin Heffernan, name: Artyom Kozhevnikov, name: Christophe Ropers, name: Eduardo Sánchez, name: Charles-Eric Saint-James, name: Ioannis Tsiamas, name: Chierh Cheng, name: Joe Chuang, name: Paul-Ambroise Duquenne, name: Mark Duppenthaler, name: Nate Ekberg, name: Cynthia Gao, name: Pere Lluís Huguet Cabot, name: João Maria Janeiro, name: Jean Maillard, name: Gabriel Mejia Gonzalez, name: Holger Schwenk, name: Edan Toledo, name: Arina Turkatenko, name: Albert Ventayol-Boada, name: Rashel Moritz, name: Alexandre Mourachko, name: Surya Parimi, name: Mary Williamson, name: Shireen Yates, name: David Dale, name: Marta R. Costa-jussà
Organization: AI at Meta

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 systems still offer only limited coverage: about 200 languages on the target side, and maybe a few hundreds more on the source side, supported due to cross-lingual transfer. And even these numbers have been hard to evaluate due to the lack of reliable benchmarks and metrics.
  We present Omnilingual Machine Translation (OMT), the first MT system supporting more than 1,600 languages. This scale is enabled by a comprehensive data strategy that integrates large public multilingual corpora with newly created datasets, including manually curated MeDLEY bitext.
  We explore two ways of specializing a Large Language model (LLM) for machine translation: as a decoder-only model (OMT-LLaMA) or as a module in an encoder-decoder architecture (OMT-NLLB). Notably, all our 1B to 8B parameter models match or exceed the MT performance of a 70B LLM baseline, revealing a clear specialization advantage and enabling strong translation quality in low-compute settings. Moreover, our evaluation of English-to-1,600 translations further shows that while baseline models can interpret undersupported languages, they frequently fail to generate them with meaningful fidelity; OMT-LLaMA models substantially expand the set of languages for which coherent generation is feasible. Additionally, OMT models improve in cross-lingual transfer, being close to solving the "understanding" part of the puzzle in MT for the 1,600 evaluated. Our leaderboard and main human-created evaluation datasets (BOUQuET and Met-BOUQuET) are dynamically evolving towards Omnilinguality and freely available.