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.

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.

1. Select 20 diverse tasks: Choose tasks such as summarizing, writing formal emails, and reasoning about a Dutch legal text.
2. Define the 'golden' standard: Write down what a 100% perfect response entails for each task. What absolutely cannot be missing?
3. 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.