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New dataset quantifies AI capability transfer across 15 languages

By PulseAugur Editorial · [1 sources] ·

A new dataset called Multilingual GSM-Symbolic has been introduced to study how capabilities transfer across different languages in AI models. This dataset, comprising 30,000 matched question-answer pairs in 15 languages, reveals that model size, language resource level, and reasoning ability are key predictors of cross-lingual performance. The findings suggest that larger models and stronger reasoning skills help bridge the performance gap between low- and high-resource languages, with implications for model development and evaluation strategies. AI

IMPACT Provides a framework to better understand and predict AI model performance across languages, potentially reducing evaluation costs.

RANK_REASON The cluster contains a new academic paper introducing a novel dataset and analysis framework for AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset quantifies AI capability transfer across 15 languages

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The cluster contains a new academic paper introducing a novel dataset and analysis framework for AI research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kenneth Enevoldsen, Riley Herchert, Sofie Mosegaard, Dan Saattrup Smart, Simon Enni, Isaac Chung, Sofie Bruun, Ayush Sunil Munot, Max M\"uller-Eberstein, Adnan El-Assadi, Elisa Bassignana, Gianluca Barmina, Hafsteinn Einarsson, Iben Nyholm Debess, Linda … ·

    Multilingual GSM-Symbolic: What determines capability transfer across languages?

    arXiv:2610.03367v1 Announce Type: new Abstract: We understand little about how capabilities acquired in one language carry over to another, or what governs this transfer: evaluations rely on incomparable, saturation-prone datasets and rarely examine its determinants jointly. Iden…

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