Danoliteracy of Generative Large Language Models

Søren Vejlgaard Holm; Lars Kai Hansen; Martin Carsten Nielsen

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arXiv · 2410.22839

Danoliteracy of Generative Large Language Models

Søren Vejlgaard Holm·Lars Kai Hansen·Martin Carsten Nielsen

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Abstract

The language technology moonshot moment of Generative Large Language Models (GLLMs) was not limited to English: These models brought a surge of technological applications, investments, and hype to low-resource languages as well. However, the capabilities of these models in languages such as Danish were, until recently, difficult to verify beyond qualitative demonstrations due to a lack of applicable evaluation corpora. We present a GLLM benchmark to evaluate \emph{Danoliteracy}, a measure of Danish language and cultural competency across eight diverse scenarios such as Danish citizenship tests and abstractive social media question answering. This limited-size benchmark was found to produce a robust ranking that correlates to human feedback at $\rho \sim 0.8$ with GPT-4 and Claude Opus models achieving the highest rankings. Analyzing these model results across scenarios, we find one strong underlying factor explaining $95\%$ of scenario performance variance for GLLMs in Danish, suggesting a $g$ factor of model consistency in language adaptation.

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BibTeX RIS

Søren Vejlgaard Holm, Lars Kai Hansen, Martin Carsten Nielsen. 2024-10-30. Danoliteracy of Generative Large Language Models. https://arxiv.org/abs/2410.22839

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