The paper reports from a representativeness study covering Sámi languages in a Norwegian national web crawl. We extracted linguistic content from HTML in a considerable portion of the archive and applied language identification on paragraph-level. By aggregating the results on domain-name level, we were able to quantify the amount of Sámi language content in the web archive and identify contributing domains. Further, by comparing the URLs of the national crawl with a Sámi reference corpus, we identified domain names that were lacking in the national crawl and domains that lack depth. The results will be used to increase the coverage of Sámi language content in future web crawls and acquisitions.

The use of copyrighted materials in training language models raises critical legal and ethical questions. This paper presents a framework for and the results of empirically assessing the impact of publisher-controlled copyrighted corpora on the performance of generative large language models (LLMs) for Norwegian. When evaluated on a diverse set of tasks, we found that adding both books and newspapers to the data mixture of LLMs tend to improve their performance, while the addition of fiction works seems to be detrimental. Our experiments could inform the creation of a compensation scheme for authors whose works contribute to AI development.