BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data

Jaap Jumelet, Abdellah Fourtassi, Akari Haga, Bastian Bunzeck, Bhargav Shandilya, Diana Galv\'an-Sosa, Faiz Ghifari Haznitrama, Francesca Padovani, Francois Meyer, Hai Hu, Julen Etxaniz, Laurent Pr'evot, Linyang He, Mar\'ia Grandury, Mila Marcheva, Negar Foroutan, Nikitas Theodoropoulos, Pouya Mir Mohammad Sadeghi, Siyuan Song, Suchir Salhan, Susana Zhou, Yurii Paniv, Ziyin Zhang, Arianna Bisazza, Alexander Scott Warstadt, Leshem Choshen · EACL · 2026

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Abstract

We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. We curate developmentally plausible pretraining data aiming to cover the equivalent of 100M English words of content in each of 45 languages. We compile evaluation suites and train baseline models in each language. BabyBabelLM aims to facilitate multilingual pretraining and cognitive modeling.

How to cite

@article{jumelet2025babybabellm,
title={BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data},
  author={Jaap Jumelet and Abdellah Fourtassi and Akari Haga and Bastian Bunzeck and Bhargav Shandilya and Diana Galv{\'a}n-Sosa and Faiz Ghifari Haznitrama and Francesca Padovani and Francois Meyer and Hai Hu and Julen Etxaniz and Laurent Pr'evot and Linyang He and Mar{\'i}a Grandury and Mila Marcheva and Negar Foroutan and Nikitas Theodoropoulos and Pouya Mir Mohammad Sadeghi and Siyuan Song and Suchir Salhan and Susana Zhou and Yurii Paniv and Ziyin Zhang and Arianna Bisazza and Alexander Scott Warstadt and Leshem Choshen},
  journal={EACL},
  year={2026},
  volume={abs/2510.10159},
  url={https://api.semanticscholar.org/CorpusID:282058670}
}

References

See the full reference list in the paper.