Informational Antilocality and the Locality Bias in LLMs

Andrew McInnerney; Shane Storks; Steven Abney; Richard L. Lewis

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arXiv · 2608.27760

Informational Antilocality and the Locality Bias in LLMs

Andrew McInnerney·Shane Storks·Steven Abney·Richard L. Lewis

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Abstract

We consider the ability of transformer-based language models (LLMs) to learn what we call k-antilocal languages, i.e., languages that have no mutual information across any span of $k$ contiguous symbols. We construct such languages with increasing $k$, finding that LLMs trained on them achieve comparable cross-entropy loss regardless of antilocality, but converge more slowly on more antilocal languages. Our findings support the idea that non-local dependencies are more difficult to learn, but the evidence for this bias comes from learning speed rather than learning success.

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BibTeX RIS

Andrew McInnerney, Shane Storks, Steven Abney, Richard L. Lewis. 2026-08-27. Informational Antilocality and the Locality Bias in LLMs. https://arxiv.org/abs/2608.27760

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