# SDUs DAISY: A Benchmark for Danish Culture

arXiv: https://arxiv.org/abs/2601.19930 · HTML: https://mlsanity.com/paper/2601.19930/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 11 Jan 2026.

## Builds on (most cited first, 14 of 14)

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- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Humanity's Last Exam](https://mlsanity.com/paper/2501.14249/) (2025)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages](https://mlsanity.com/paper/2406.09948/) (2024)
- [DaCy: A Unified Framework for Danish NLP](https://mlsanity.com/paper/2107.05295/) (2021)
- [MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages](https://mlsanity.com/paper/2509.04111/) (2025)
- [DaKultur: Evaluating the Cultural Awareness of Language Models for Danish with Native Speakers](https://mlsanity.com/paper/2504.02403/) (2025)
- [Dynaword: From One-shot to Continuously Developed Datasets](https://mlsanity.com/paper/2508.02271/) (2025)

## Abstract (arXiv)

We introduce Daisy, a factual knowledge benchmark for Danish cultural heritage, based on curated topics from the Danish Culture Canon 2006. For each artifact in the culture canon, we query the corresponding Wikipedia page and have a language model generate a diverse set of questions. Within each artifact, we sample both central and peripheral questions, testing not only mainstream information but also the deeper, defining elements of Danish cultural heritage as identified by the Canon committee. Each question-answer pair is manually approved or corrected, yielding a final dataset of 741 closed-ended question-answer pairs ranging from archaeological findings dated to 1300 BCE and 18th-century poems and musical pieces through to contemporary pop music, Danish design, and architecture. Baseline results on our benchmark show that contemporary language models (GPT-OSS-120B & 20B, Llama-3.3-70B, Gemma3-27B, and Mistral-3.1-24B) perform surprisingly poorly on this task, despite the tested factual knowledge being available on Wikipedia. The best-performing model, Llama-3.3-70B, attains only 0.17 BLEU and an F1 score of 0.27, demonstrating the benchmark's difficulty. The questions, model benchmarks, and the evaluation tool are publicly available at https://github.com/schneiderkamplab/SDU-Daisy and under schneiderkamplab/SDU-Daisy on Hugging Face Datasets.