GreekMMLU

GreekMMLU is a native-sourced benchmark for evaluating massive multitask language understanding in Greek, built from authentic Greek exam-style multiple-choice questions (MCQ) rather than machine-translated English benchmarks.

• 21,805 questions across 45 subjects
• 4 high-level groups: STEM, Humanities, Social Sciences, Other
• Difficulty/education levels spanning Primary → Secondary → University → Professional (+ an N/A bucket)
• Public vs. private split for contamination-resistant evaluation: 16,857 public/4,948 private (leaderboard)

Structure

• Configs: Each subject is a separate configuration (subset).
• Splits:
  • test: Public test set (High volume)
  • dev: Dev/Few-shot set (5-shot)

Usage

from datasets import load_dataset

# Load specific subject 
ds = load_dataset("dascim/GreekMMLU", "Agriculture_Professional")

# Load all data combined
ds = load_dataset("dascim/GreekMMLU", "All")

Links

• Our paper: https://www.arxiv.org/abs/2602.05150

• GitHub: https://github.com/mersinkonomi/GreekMMLU

• Private leaderboard: https://huggingface.co/spaces/yangzhang33/GreekMMLU-Leaderboard

What makes GreekMMLU different?

Most “Greek MMLU” style evaluations rely on machine translation from English. Instead, GreekMMLU uses original Greek content sourced or authored from real educational/professional assessments, aiming to preserve:

• Greek morphology and punctuation
• Greek-specific cultural/institutional knowledge (e.g., Greek History, Greek Traditions)
• Realistic exam difficulty calibration

Task format

Multiple choice, 2–4 options, exactly one correct. The harness prompt uses Greek option labels (Α, Β, Γ, Δ) for a fully native evaluation setup.

Subjects (high-level)

GreekMMLU includes 45 subjects, grouped into:

• Humanities (e.g., Art; Greek History; Greek Literature; Greek Mythology; Law; World Religions)
• STEM (e.g., Mathematics; Physics; Computer Science; Electrical Engineering; Medicine)
• Social Sciences (e.g., Economics; Education; Government & Politics; Modern Greek Language; Accounting)
• Other (e.g., Driving Rules; General Knowledge; Maritime Safety and Rescue Operations) See the paper for the full taxonomy and educational-level breakdown.

Citation

If you use GreekMMLU in your work, please cite the paper:

@article{zhang2026greekmmlu,
  title={GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek},
  author={Zhang, Yang and Konomi, Mersin and Xypolopoulos, Christos and Divriotis, Konstantinos and Skianis, Konstantinos and Nikolentzos, Giannis and Stamou, Giorgos and Shang, Guokan and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2602.05150},
  year={2026}
}