Structural priors for data-efficient language learning
Quick summary
arXiv:2609.11505v1 Announce Type: cross 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 langu
Key takeaways
- arXiv:2609.11505v1 Announce Type: cross 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.
Why it matters
“Structural priors for data-efficient language learning” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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