arXiv — NLP / Computation & Language · · 3 min read

Informational Antilocality and the Locality Bias in LLMs

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Computer Science > Computation and Language

arXiv:2608.27760 (cs)
[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.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2608.27760 [cs.CL]
  (or arXiv:2608.27760v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.27760
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shane Storks [view email]
[v1] Thu, 27 Aug 2026 22:49:08 UTC (271 KB)
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