Informational Antilocality and the Locality Bias in LLMs

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.

Computation and Language (cs.CL)

arXiv:2608.27760 [cs.CL]

(or arXiv:2608.27760v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.27760 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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