16 citations · 16 across the 4 of their papers we have counts for
10 papers · 1 filter
MultiSynt/MT: Trillion-Token Multi-Parallel Pre-Training Data Translated Across 36 Languages
Maximilian Idahl, Jörg Tiedemann, Sampo Pyysalo +19
Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synthetic parallel corpus with approxi…
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…
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…
The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective
Javier de la Rosa, Vladislav Mikhailov, Lemei Zhang +16
The use of copyrighted materials in training language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessi…
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…
Direct parsing to sentiment graphs
David Samuel, Jeremy Barnes, Robin Kurtz +3
This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text. We advance th…