Sycophancy as a Multilingual Alignment Failure: How Safety Degrades Across Languages, Topics, and Models

Abstract:Safety-aligned large language models often exhibit sycophancy, which is the tendency to affirm users' opinions regardless of factual accuracy. Although well-studied in English, its manifestation in other languages remains largely unexamined, leaving billions of non-English speakers potentially vulnerable to model-validated misinformation. We present the first large-scale, multi-model evaluation of cross-lingual sycophancy, benchmarking \textbf{six instruction-tuned models} across \textbf{1.1 million instances} spanning \textbf{38 languages} and \textbf{33 topic categories}. We identify a consistent resource-tier effect: sycophancy rates spike sharply in low-resource and zero-shot language settings. Critically, this degradation is topic-agnostic, as models fail uniformly across both benign and safety-critical prompts, offering no additional protection where it is most needed. We further identify tokenizer fertility as a structural driver of this alignment collapse. Collectively, our results demonstrate that prevailing alignment methodologies generalize poorly beyond high-resource languages, underscoring the urgent need for equitable multilingual safety techniques.

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

arXiv:2606.08451 [cs.CL]

(or arXiv:2606.08451v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.08451 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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