Recent work suggests that obscure language registers can weaken large language model refusal behavior, especially when paired with black-box prompt optimization. It remains unclear whether failures stem from non-standard surface form, culturally grounded framing, or optimization over an expressive prompt-strategy space. We study Chinese registers by extending a classical-Chinese red-teaming framework to Shanghainese and Cantonese and running a 36-cell ablation across surface forms, strategy-bank variants, and two target models. The main finding is corrective: dialectal surface form is neither necessary nor sufficient for high attack success. Non-optimized English, Mandarin, and naive dialect translations remain below 8\% attack success rate, whereas all conditions that retain an optimizer-controlled strategy bank reach 98--100\%. A culture-neutral generic strategy bank reaches the same ceiling at near-single-query cost, further indicating that strategy-bank expressiveness, rather than dialectal or cultural content, accounts for most of the observed effect. Dialect choice still affects query efficiency and response severity, and qualitative coding identifies recurring high-level mechanisms such as semantic glossing and procedural scaffolding. We qualify the absolute ceiling-level attack success rate by showing sensitivity to judge calibration and noting that the design does not fully separate one-shot strategy construction from iterative search.
It is argued that collapsing cultural competence into a single scalar incentivizes "probabilistic flattening," and that recognizing the unalignable class is a precondition for AI that respects, rather than erases, cultural divergence.
The study traces this dialect tax across the natural language processing pipeline, suggesting that the dialect tax is encoded and accumulated not by any one step in isolation, but at every step of the language modeling process.
Elle Michelle Yang, Mark Chen, Jerry Tworek et al.· 0 citations
Safety-aligned language models often refuse a harmful request stated directly but answer the same request inside a role-play or narrative wrapper. We measure this vulnerability across languages and registers: attack success on Qwen3-1.7B is already 89.4% in English and 93.0% in modern Chinese, and reaches 95.7% in Clas...
Zhan-Kai Ye, Yan-Ning Wang, Yu-Kai Jin et al.· 0 citations
CNeo-Bench, a benchmark of 4,759 Chinese neologisms with reference definitions, is introduced, organized into five top-level categories and nine subcategories by the linguistic mechanism behind each expression, paired with a two-tier evaluation framework that separates whether a model can describe a neologism from whet...
Kai-Yan Zhao, Zhong-Tao Miao, Zhe-Yong Xie et al.· 0 citations
As large language models increasingly shape public discourse, this study evaluates how US and China-based models govern access to politically sensitive history in US English and Mandarin Chinese. Across 2,400 interactions covering sixty globally significant events, we examined how prompt configurations modulate LLM res...
Nisha Dileep, Firoz Noorbasha, Jordan Baillie et al.· Journal of East Asia Securit...· 0 citations
The results show that pluralistic prompting is a practical lever for value-conditional behavior and a fertile instrument for creative AI exploration in large language models with ten-principle constitutions.
Arjun Balaji, Aarushi Nema, Neha Kamath et al.· 0 citations