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The Nordic Pile: A 1.2TB Nordic Dataset for Language Modeling

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arxiv 2303.17183 v1 pith:ZQUQ656Q submitted 2023-03-30 cs.CL cs.AI

classification cs.CLcs.AI
keywords llmsnordicdatasetlanguagestextdatahigh-qualitylanguage
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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. This means that it may be challenging to build LLMs for smaller languages such as Nordic ones, where the availability of text corpora is limited. In order to facilitate the development of the LLMS in the Nordic languages, we curate a high-quality dataset consisting of 1.2TB of text, in all of the major North Germanic languages (Danish, Icelandic, Norwegian, and Swedish), as well as some high-quality English data. This paper details our considerations and processes for collecting, cleaning, and filtering the dataset.

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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. Dynaword: From One-shot to Continuously Developed Datasets

    cs.CL 2025-08 conditional novelty 5.0 of 10

    Danish Dynaword packages 4.8B tokens of openly licensed Danish text into a continuously versioned, test-gated corpus that improves language-model perplexity compared with Danish Gigaword.

  2. Aligning Language Models for Icelandic Legal Text Summarization

    cs.CL 2025-04 conditional novelty 5.0 of 10

    DPO and RLHF preference training give inconsistent gains for Icelandic legal summarization, and ROUGE scores clash with expert human evaluation.

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