aiqbench analysis
This research introduces BanglaSafe, a critical benchmark exposing significant safety gaps in large language models when evaluated on Bengali content using culturally specific harm categories. The study reveals that writing style within the same language exerts a stronger influence on model safety failures than the language switch itself, while also highlighting the inability of current classifiers to reliably assess non-English outputs. These findings underscore the urgent need for linguistically diverse and context-aware safety evaluation frameworks beyond English-centric standards.
Register Shifts Break LLM Safety: A Bengali Benchmark with Culturally Grounded Harms
arXiv:2608.22335v1 Announce Type: cross Abstract: Bengali is the seventh-most-spoken language globally, yet LLM safety evaluation remains overwhelmingly English-centric. We introduce BanglaSafe, a benchmark of 879 Bengali prompts combining 309 natively authored prompts with 570 expert-reviewed prompts, spanning 17 culturally grounded harm categories and five prompting conditions that vary language, writing style, and authority framing. Evaluating 18 frontier LLMs, we find that over half of all responses are unsafe or partially unsafe (53.6%) while 14.7% contains strictly harmful content, and that the strongest observed effect is not the switch from English to Bengali but the choice of writing style within Bengali: the same harmful request phrased as a formal newspaper investigation succeeds 17 percentage points more often than the same request phrased as a casual message, with no adversarial engineering involved. We further show that existing safety classifiers struggle to reliably evaluate Bengali content, with even frontier models failing on nearly half of all cases.
Abstract:Bengali is the seventh-most-spoken language globally, yet LLM safety evaluation remains overwhelmingly English-centric. We introduce BanglaSafe, a benchmark of 879 Bengali prompts combining 309 natively authored prompts with 570 expert-reviewed prompts, spanning 17 culturally grounded harm categories and five prompting conditions that vary language, writing style, and authority framing. Evaluating 18 frontier LLMs, we find that over half of all responses are unsafe or partially unsafe (53.6%) while 14.7% contains strictly harmful content, and that the strongest observed effect is not the switch from English to Bengali but the choice of writing style within Bengali: the same harmful request phrased as a formal newspaper investigation succeeds 17 percentage points more often than the same request phrased as a casual message, with no adversarial engineering involved. We further show that existing safety classifiers struggle to reliably evaluate Bengali content, with even frontier models failing on nearly half of all cases.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2608.22335 [cs.CL]
(or arXiv:2608.22335v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.22335 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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