Structural priors for data-efficient language learning
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 ...
| Authors | Yana Veitsman, Jonas Mayer Martins, Jonathan Lautenschlager et al. |
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| Primary category | cs.CL |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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