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MACE-MP-0: A Foundation Model for Atomistic Systems

I. Batatia, P. Benner, Y. Chiang, R. Elena, D. P. Kovács

Featured July 27, 2026

Original Paper

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Simply

To make AI language models fair for all European languages, experts are having students human-translate a big test, making sure it's perfect and culturally right, instead of just using computers.

In depth
The paper details a large-scale initiative to create a high-quality, human-translated version of the MMLU benchmark for evaluating Large Language Models (LLMs) across 11 European languages. This is achieved by integrating the translation process into an authentic project-based learning environment for master's students, ensuring linguistic and cultural accuracy while proactively avoiding issues like the "ouroboros effect" common in machine-translated datasets.

Key Takeaways

  • 1
    The project produces a human-translated version of the MMLU dataset into 11 European languages, ensuring high linguistic quality and cultural appropriateness for LLM evaluation.
  • 2
    It integrates this technical objective with an authentic project-based learning environment, providing master's students with professional training in translation, revision, and project management.
  • 3
    The methodology explicitly avoids machine translation to prevent the "ouroboros effect" and addresses US-centric biases through an adaptive localisation strategy.

Conceptual Flow

HIGH LEVEL
1
Methodology: Human-Powered Localisation

Instead of using computers to translate, real students and experts carefully translate test questions into many European languages.

English Test Questions
Student Translators
Expert Reviewers
Human Translation & Review
High-Quality European Test Questions
2
Results: Better AI Evaluation & Training

This new way of translating helps make AI models work better for everyone and teaches students important job skills.

Old Computer-Translated Tests
New Human-Translated Tests
Better AI Evaluation
Fairer AI Models
Trained Students