1 citations · 1 across the 2 of their papers we have counts for
5 papers
Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance
Amanda Myntti, Jenna Kanerva, Veronika Laippala +1
In this paper, we show that high-performing embedding models organize their embedding spaces in a consistent way. We evaluate 25 contemporary embedding models on five MTEB tasks sp…
Automatic register identification for the open web using multilingual deep learning
Erik Henriksson, Amanda Myntti, Saara Hellström +3
This article presents multilingual deep learning models for identifying web registers -- text varieties such as news reports and discussion forums -- across 16 languages. We introd…
Register Always Matters: Analysis of LLM Pretraining Data Through the Lens of Language Variation
Amanda Myntti, Erik Henriksson, Veronika Laippala +1
Pretraining data curation is a cornerstone in Large Language Model (LLM) development, leading to growing research on quality filtering of large web corpora. From statistical qualit…
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…
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
Taishi Nakamura, Mayank Mishra, Simone Tedeschi +42
Pretrained language models are an integral part of AI applications, but their high computational cost for training limits accessibility. Initiatives such as Bloom and StarCoder aim…