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        <title>Structural priors for data-efficient language learning</title>
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            <title>Proceedings of the Second BabyLM Workshop</title>
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            <namePart type="given">Leshem</namePart>
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            <namePart type="given">Ryan</namePart>
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    <abstract>Efficient language learning requires methods to reduce the reliance on large data and computational resources. We investigate *structural transfer*: First training models on non-language data to induce useful priors for natural language. This approach is a form of weight initialization for multilingual language modeling. We evaluate transfer via next-token-prediction loss, weight shifts in the model, and downstream linguistic benchmarks. Several symbolic data types—notably music, probabilistic grammars, and cellular automata—yield lower language-modeling loss than random initialization. These gains coincide with smaller weight shifts during subsequent language training, suggesting that structural transfer positions models in a more favorable region of the parameter space. However, a lower loss does not translate consistently into better downstream linguistic performance, and transfer from non-language data is less efficient than additional language data. We conclude that non-language data can serve as a partial substitute for language data for the training objective of next-token prediction but does not reliably support broader linguistic generalization.</abstract>
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%0 Conference Proceedings
%T Structural priors for data-efficient language learning
%A Veitsman, Yana
%A Mayer Martins, Jonas
%A Lautenschlager, Jonathan
%A Beinborn, Lisa
%Y Choshen, Leshem
%Y Cotterell, Ryan
%Y Gul, Mustafa Omer
%Y Jumelet, Jaap
%Y Linzen, Tal
%Y Mueller, Aaron
%Y Salhan, Suchir
%Y Shah, Raj Sanjay
%Y Warstadt, Alex
%Y Wilcox, Ethan Gotlieb
%S Proceedings of the Second BabyLM Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-475-0
%F veitsman-etal-2026-structural
%X Efficient language learning requires methods to reduce the reliance on large data and computational resources. We investigate *structural transfer*: First training models on non-language data to induce useful priors for natural language. This approach is a form of weight initialization for multilingual language modeling. We evaluate transfer via next-token-prediction loss, weight shifts in the model, and downstream linguistic benchmarks. Several symbolic data types—notably music, probabilistic grammars, and cellular automata—yield lower language-modeling loss than random initialization. These gains coincide with smaller weight shifts during subsequent language training, suggesting that structural transfer positions models in a more favorable region of the parameter space. However, a lower loss does not translate consistently into better downstream linguistic performance, and transfer from non-language data is less efficient than additional language data. We conclude that non-language data can serve as a partial substitute for language data for the training objective of next-token prediction but does not reliably support broader linguistic generalization.
%U https://aclanthology.org/2026.babylm-main.17/
%P 209-226

Markdown (Informal)

[Structural priors for data-efficient language learning](https://aclanthology.org/2026.babylm-main.17/) (Veitsman et al., BabyLM 2026)

• Structural priors for data-efficient language learning (Veitsman et al., BabyLM 2026)

ACL

• Yana Veitsman, Jonas Mayer Martins, Jonathan Lautenschlager, and Lisa Beinborn. 2026. Structural priors for data-efficient language learning. In Proceedings of the Second BabyLM Workshop, pages 209–226, Budapest, Hungary. Association for Computational Linguistics.