@inproceedings{daason-etal-2026-optimized,
  title = {Optimized for AI: Curating the Icelandic Gigaword Corpus for Stable LLM Training },
  author = {Daðason, Jón Friðrik and Steingrímsson, Steinþór},
  booktitle = {Proceedings of the 12th Workshop on Challenges in the Management of Large Corpora},
  month = {May},
  year = {2026},
  pages = {49--56},
  address = {Palma, Mallorca, Spain},
  publisher = {European Language Resources Association (ELRA)},
  editor = {Bański, Piotr and Knight, Dawn and Kupietz, Marc and Witt, Andreas and Wróblewska, Alina},
  doi = {10.63317/3uatbht8mdrf},
  abstract = {The Icelandic Gigaword Corpus (IGC) is a primary resource for Icelandic NLP, with its current version containing 2.7 billion words of curated text. The IGC is traditionally distributed in a TEI-XML format, a hierarchical structure that allows for rich linguistic annotation and metadata. However, this format introduces significant friction for modern machine learning workflows. Even high-quality curated corpora have been found to contain "unwanted" text sequences – such as fragmented lists or repetitive boilerplate that may trigger instabilities during training of large language models. In this paper, we present a new processing pipeline designed to optimize the IGC for AI development. We describe a filtering approach focusing on training stability, including fuzzy deduplication to reduce the risk of data leakage, with the aim to provide high-quality data for stable model convergence. Furthermore, we introduce a new JSONL distribution format that bridges the gap between TEI-XML and machine-actionable data, facilitating easier access and safer training for models aiming to work with Icelandic.}
}