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AT-Bench: Austrian German Benchmark

300 multiple-choice questions testing whether an LLM actually understands Austrian German — not just German.

The problem

Every German benchmark treats German as one language. But ask GPT what "Obers" means and half the time it guesses wrong. Ask it about the Bezirksgericht and it describes the German court system. Austrian German is an official language variety spoken by 9 million people, and models consistently get it wrong.

Tasks

Task Count What it tests
vocabulary 80 Do you know Erdapfel = potato?
knowledge 80 Austrian geography, institutions, culture
register 50 Is this text AT, DE, or CH?
culture 50 Traditions, holidays, customs
legal_basics 40 ABGB, Bezirksgericht, basic legal system

Difficulty split: 40% easy, 40% medium, 20% hard.

Format

{
  "task": "vocabulary",
  "question": "Welches Wort verwendet man in Oesterreich fuer 'Sahne'?",
  "choices": ["Rahm", "Schmand", "Obers", "Schmetten"],
  "correct": "Obers",
  "difficulty": "easy"
}

Usage

from datasets import load_dataset
ds = load_dataset("Laborator/austrian-german-benchmark")

What I want to do with this

Run the major open models through it and publish a leaderboard. My hypothesis is that even large models score below 70% on the Austrian-specific questions. If your model does better, open a discussion — I'd genuienly like to know.

License

Apache 2.0

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