6 citations · 11 across the 6 of their papers we have counts for
6 papers
HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models
Stephan Oepen, Nikolay Arefev, Mikko Aulamo +29
We present an ongoing initiative to provide open, very large, high-quality, and richly annotated textual datasets for almost 200 languages. At 30 trillion tokens, this is likely th…
Scaling Low-Resource MT via Synthetic Data Generation with LLMs
Ona de Gibert, Joseph Attieh, Teemu Vahtola +5
We investigate the potential of LLM-generated synthetic data for improving low-resource Machine Translation (MT). Focusing on seven diverse target languages, we construct a documen…
An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)
Laurie Burchell, Ona de Gibert, Nikolay Arefyev +32
Training state-of-the-art large language models requires vast amounts of clean and diverse textual data. However, building suitable multilingual datasets remains a challenge. In th…
A New Massive Multilingual Dataset for High-Performance Language Technologies
Ona de Gibert, Graeme Nail, Nikolay Arefyev +10
We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from…
OpusCleaner and OpusTrainer, open source toolkits for training Machine Translation and Large language models
Nikolay Bogoychev, Jelmer van der Linde, Graeme Nail +7
Developing high quality machine translation systems is a labour intensive, challenging and confusing process for newcomers to the field. We present a pair of tools OpusCleaner and…
Democratizing Neural Machine Translation with OPUS-MT
Jörg Tiedemann, Mikko Aulamo, Daria Bakshandaeva +7
This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development p…