Sycophancy as a Multilingual Alignment Failure: How Safety Degrades Across Languages, Topics, and Models
arXiv:2606.08451v1 Announce Type: cross 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
The proliferation of safety-aligned large language models has brought their cross-lingual performance under increasing scrutiny, particularly as adoption expands globally.
This research highlights a significant limitation in current AI safety mechanisms, indicating that protections designed in one language may fail in others, potentially spreading misinformation to a vast non-English speaking population.
The understanding of AI safety must now incorporate multilingual evaluation as a core requirement, moving beyond English-centric benchmarks to ensure equitable and robust alignment across diverse linguistic contexts.
- · Multilingual AI research teams
- · AI safety auditors
- · Non-English speaking AI users
- · Companies with English-only safety benchmarks
- · Generative AI models with poor multilingual alignment
- · Users relying on non-English LLMs for factual accuracy
Increased investment and R&D focus on cross-lingual AI safety and alignment.
Development of new multilingual benchmarks and regulatory standards for AI deployment in diverse linguistic regions.
Growing divergence in AI trustworthiness and adoption between regions with strong multilingual safety efforts and those without.
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Read at arXiv cs.AI