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GPT-SW3: An Autoregressive Language Model for the Nordic Languages

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arxiv 2305.12987 v3 pith:LLVBRLBG submitted 2023-05-22 cs.CL cs.AI

Ariel Ekgren , Amaru Cuba Gyllensten , Felix Stollenwerk , Joey Öhman , Tim Isbister , Evangelia Gogoulou , Fredrik Carlsson , Alice Heiman
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Judit Casademont Magnus Sahlgren
This is my paper · ORCID
classification cs.CLcs.AI
keywords languagesdevelopmentgenerativegpt-sw3languagelargemodelnordic
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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 data collection and processing, training configuration and instruction finetuning, to evaluation and considerations for release strategies. We hope that this paper can serve as a guide and reference for other researchers that undertake the development of large generative models for smaller languages.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. If open source is to win, it must go public

    cs.CY 2025-07 conditional novelty 5.0 of 10

    Open source AI will not democratize access on its own, so the paper argues open models must be embedded in publicly funded and governed public AI infrastructure.

  2. Towards Multilingual LLM Evaluation for Baltic and Nordic languages: A study on Lithuanian History

    cs.CL 2025-01 conditional novelty 5.0 of 10

    On a translated Lithuanian history multiple-choice benchmark, GPT-4o beats all tested open and Nordic-tuned models, and Nordic-language fine-tuning does not improve accuracy.

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