# DaLA: Danish Linguistic Acceptability Evaluation Guided by Real World Errors

arXiv: https://arxiv.org/abs/2512.04799 · HTML: https://mlsanity.com/paper/2512.04799/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 4 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs](https://mlsanity.com/paper/2504.02768/) (2025)
- [RuCoLA: Russian Corpus of Linguistic Acceptability](https://mlsanity.com/paper/2210.12814/) (2022)
- [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://mlsanity.com/paper/2109.12053/) (2021)
- [DaLAJ - a dataset for linguistic acceptability judgments for Swedish: Format, baseline, sharing](https://mlsanity.com/paper/2105.06681/) (2021)
- [MELA: Multilingual Evaluation of Linguistic Acceptability](https://mlsanity.com/paper/2311.09033/) (2023)
- [JCoLA: Japanese Corpus of Linguistic Acceptability](https://mlsanity.com/paper/2309.12676/) (2023)
- [NoCoLA: The Norwegian Corpus of Linguistic Acceptability](https://mlsanity.com/paper/2306.07790/) (2023)

## Built on by (most cited first, 3 of 3)

- [F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World](https://mlsanity.com/paper/2603.19223/) (2026)
- [ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World](https://mlsanity.com/paper/2605.15081/) (2026)
- [CroCo: Cross-Lingual Contrastive Preference Tuning on Self-Generations](https://mlsanity.com/paper/2605.26293/) (2026)

## Abstract (arXiv)

We present an enhanced benchmark for evaluating linguistic acceptability in Danish. We first analyze the most common errors found in written Danish. Based on this analysis, we introduce a set of fourteen corruption functions that generate incorrect sentences by systematically introducing errors into existing correct Danish sentences. To ensure the accuracy of these corruptions, we assess their validity using both manual and automatic methods. The results are then used as a benchmark for evaluating Large Language Models on a linguistic acceptability judgement task. Our findings demonstrate that this extension is both broader and more comprehensive than the current state of the art. By incorporating a greater variety of corruption types, our benchmark provides a more rigorous assessment of linguistic acceptability, increasing task difficulty, as evidenced by the lower performance of LLMs on our benchmark compared to existing ones. Our results also suggest that our benchmark has a higher discriminatory power which allows to better distinguish well-performing models from low-performing ones.