# Informational Antilocality and the Locality Bias in LLMs

> Source: <https://arxiv.org/abs/2608.27760>
> Published: 2026-08-31 04:00:00+00:00

# Computer Science > Computation and Language

[Submitted on 27 Aug 2026]

# Title:Informational Antilocality and the Locality Bias in LLMs

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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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