4 citations · 14 across the 6 of their papers we have counts for
7 papers
SWEb: A Large Web Dataset for the Scandinavian Languages
Tobias Norlund, Tim Isbister, Amaru Cuba Gyllensten +4
This paper presents the hitherto largest pretraining dataset for the Scandinavian languages: the Scandinavian WEb (SWEb), comprising over one trillion tokens. The paper details the…
GPT-SW3: An Autoregressive Language Model for the Nordic Languages
Ariel Ekgren, Amaru Cuba Gyllensten, Felix Stollenwerk +7
This paper details the process of developing the first native large generative language model for the Nordic languages, GPT-SW3. We cover all parts of the development process, from…
The Nordic Pile: A 1.2TB Nordic Dataset for Language Modeling
Joey Öhman, Severine Verlinden, Ariel Ekgren +5
Pre-training Large Language Models (LLMs) require massive amounts of text data, and the performance of the LLMs typically correlates with the scale and quality of the datasets. Thi…
Cross-lingual Transfer of Monolingual Models
Evangelia Gogoulou, Ariel Ekgren, Tim Isbister +1
Recent studies in zero-shot cross-lingual learning using multilingual models have falsified the previous hypothesis that shared vocabulary and joint pre-training are the keys to cr…
A comparative evaluation and analysis of three generations of Distributional Semantic Models
Alessandro Lenci, Magnus Sahlgren, Patrick Jeuniaux +2
Distributional semantics has deeply changed in the last decades. First, predict models stole the thunder from traditional count ones, and more recently both of them were replaced i…
Should we Stop Training More Monolingual Models, and Simply Use Machine Translation Instead?
Tim Isbister, Fredrik Carlsson, Magnus Sahlgren
Most work in NLP makes the assumption that it is desirable to develop solutions in the native language in question. There is consequently a strong trend towards building native lan…
Federated Word2Vec: Leveraging Federated Learning to Encourage Collaborative Representation Learning
Daniel Garcia Bernal, Lodovico Giaretta, Sarunas Girdzijauskas +1
Large scale contextual representation models have significantly advanced NLP in recent years, understanding the semantics of text to a degree never seen before. However, they need…