Omnilingual MT: Machine Translation for 1,600 Languages

Abstract

Advances made through No Language Left Behind (NLLB) have demonstrated that high-quality machine translation (MT) scale to 200 languages. Later Large Language Models (LLMs) have been adopted for MT, increasing in quality but not necessarily extending language coverage. Current systems remain constrained by limited coverage and a persistent generation bottleneck: while crosslingual transfer enables models to somehow understand many undersupported languages, they often cannot generate them reliably, leaving most of the world’s 7,000 languages—especially endangered and marginalized ones—outside the reach of modern MT. Early explorations in extreme scaling offered promising proofs of concept but did not yield sustained solutions. 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, synthetic backtranslation, and mining, substantially expanding coverage across long-tail languages, domains, and registers. To ensure both reliable and expansive evaluation, we combined standard metrics with a suite of evaluation artifacts: BLASER 3 quality estimation model (reference-free), OmniTOX toxicity classifier, BOUQuET dataset (a newly created, largest-to-date multilingual evaluation collection built from scratch and manually extended across a wide range of linguistic families), and Met-BOUQuET dataset (faithful multilingual quality estimation at scale). We explore two ways of specializing an LLM for machine translation: as a decoder-only model (OMT-LLaMA) or as a module in an encoder–decoder architecture (OMT-NLLB). The former consists of a model built on LLaMA3, with multilingual continual pretraining and retrieval-augmented translation for inference-time adaptation. The latter is a model built on top of a multilingual aligned space (OmniSONAR, itself also based on LLaMA3), and introduces a training methodology that can exploit non-parallel data, allowing us to incorporate the decoder-only continuous pretraining data into the training of an encoder–decoder architecture. 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. Beyond strong out-of-the-box performance, we find that finetuning and retrieval-augmented generation offer additional pathways to improve quality for the given subset of languages when targeted data or domain knowledge is available. Our leaderboard and main humanly created evaluation datasets (BOUQuET and Met-BOUQuET) are dynamically evolving towards Omnilinguality and freely available.