2025
EMNLP
EMNLP 2025
What if I ask in alia lingua? Measuring Functional Similarity Across Languages
Abstract
AbstractHow similar are model outputs across languages? In this work, we study this question using a recently proposed model similarity metric—𝜅p—applied to 20 languages and 47 subjects in GlobalMMLU. Our analysis reveals that a model’s responses become increasingly consistent across languages as its size and capability grow. Interestingly, models exhibit greater cross-lingual consistency within themselves than agreement with other models prompted in the same language. These results highlight not only the value of 𝜅p as a practical tool for evaluating multilingual reliability, but also its potential to guide the development of more consistent multilingual systems.
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The Questioner
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio