# Testing Dutch Language Proficiency of AI Models

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# Testing Dutch Language Proficiency of AI Models

 
 Large language models (LLMs) score impressively on English benchmarks such as MMLU or GSM8K. However, these scores do not translate one-to-one to the Dutch language. Models that excel in English often struggle in Dutch with unnatural sentence structures, incorrect translations of idioms, and subtle dt-spelling errors. Targeted testing for the Dutch language is therefore crucial for production implementations.

 
 Tip: Read more about guiding output in our [guide for Dutch prompt engineering](https://gids.llmnet.nl/en/).
 

 
## Which aspects should you measure?

 A robust evaluation of an AI model for the Dutch market requires more than a simple grammar check. Focus on the following three pillars:

 
 
- Spelling & Grammar: Checking for d/t/dt errors, correct word order in subordinate clauses, and the correct use of articles (de/het).
 
- Tone & Style (Register): The model's ability to consistently use 'u' (formal) or 'jij/je' (informal) without mixing them within a single text.
 
- Idioms & Local Context: Does the model understand Dutch proverbs? Does it avoid literal translations from English (e.g., "het regent katten en honden" instead of "het regent pijpenstelen")?
 

 
## Setting up your own mini test set (Golden Dataset)

 To objectively compare models, you build a Golden Dataset: a series of fixed prompts with predefined evaluation criteria.

 
 
- Select 20 diverse tasks: Choose tasks such as summarizing, writing formal emails, and reasoning about a Dutch legal text.
 
- Define the 'golden' standard: Write down what a 100% perfect response entails for each task. What absolutely cannot be missing?
 
- Assess blindly: Generate the responses using different models and ideally have them assessed blindly (by humans or via LLM-as-a-Judge using a superior model).
 

 
## Example Scorecard

 Below is a simplified evaluation matrix. Use this as a template to structure your own tests.

 
 
 
 
 
 Evaluation aspect | 
 Model A (Placeholder) | 
 Model B (Placeholder) | 
 Weighting | 
 

 
 
 
 Spelling (dt-errors, syntax) | 
 [Score 1-10] | 
 [Score 1-10] | 
 30% | 
 

 
 Style consistency (U vs. Jij) | 
 [Score 1-10] | 
 [Score 1-10] | 
 25% | 
 

 
 Cultural & Idiomatic understanding | 
 [Score 1-10] | 
 [Score 1-10] | 
 25% | 
 

 
 Logical reasoning in Dutch | 
 [Score 1-10] | 
 [Score 1-10] | 
 20% |