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This study reveals that Large Language Models interpret imperative instructions as language-specific social acts rather than universal technical commands, causing identical semantic content to produce opposite interaction topologies in English versus Spanish. The research demonstrates that shifting from authoritative imperatives to declarative statements can drastically reduce cross-linguistic variance and alter model behavior across entire prompt blocks. These findings highlight a critical vulnerability where constitutional AI principles authored in specific linguistic registers may inadvertently create language-dependent alignment issues.
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Imperative Interference: Social Register Shapes Instruction Topology in Large Language Models

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2603.25015v2 Announce Type: replace-cross Abstract: System prompt instructions that cooperate in English compete in Spanish, with the same semantic content, but opposite interaction topology. We present instruction-level ablation experiments across four languages and four models showing that this topology inversion is mediated by social register: the imperative mood carries different obligatory force across speech communities, and models trained on multilingual data have learned these conventions. Declarative rewriting of a single instruction block reduces cross-linguistic variance by 81% (p = 0.029, permutation test). Rewriting three of eleven imperative blocks shifts Spanish instruction topology from competitive to cooperative, with spillover effects on unrewritten blocks. These findings suggest that models process instructions as social acts, not technical specifications: "NEVER do X" is an exercise of authority whose force is language-dependent, while "X: disabled" is a factual description that transfers across languages. If register mediates instruction-following at inference time, it plausibly does so during training. We state this as a testable prediction: constitutional AI principles authored in imperative mood may create language-dependent alignment. Corpus: 22 hand-authored probes against a production system prompt decomposed into 56 blocks.

Abstract:System prompt instructions that cooperate in English compete in Spanish, with the same semantic content, but opposite interaction topology. We present instruction-level ablation experiments across four languages and four models showing that this topology inversion is mediated by social register: the imperative mood carries different obligatory force across speech communities, and models trained on multilingual data have learned these conventions. Declarative rewriting of a single instruction block reduces cross-linguistic variance by 81% (p = 0.029, permutation test). Rewriting three of eleven imperative blocks shifts Spanish instruction topology from competitive to cooperative, with spillover effects on unrewritten blocks. These findings suggest that models process instructions as social acts, not technical specifications: "NEVER do X" is an exercise of authority whose force is language-dependent, while "X: disabled" is a factual description that transfers across languages. If register mediates instruction-following at inference time, it plausibly does so during training. We state this as a testable prediction: constitutional AI principles authored in imperative mood may create language-dependent alignment. Corpus: 22 hand-authored probes against a production system prompt decomposed into 56 blocks.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

arXiv:2603.25015 [cs.CL]

(or arXiv:2603.25015v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.25015 Focus to learn more arXiv-issued DOI via DataCite

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