Culturally-Adapted Red-Teaming Across East and Southeast Asian Contexts: A Methodological and Comparative Analysis
arXiv:2606.09178v1 Announce Type: cross Abstract: Multilingual safety evaluation of large language models (LLMs) has predominantly relied on direct translation (DT) of English benchmarks into target languages - an approach that converts surface-level linguistic form while failing to reflect the cultural context embedded in threat scenarios, social norms, and legal frameworks. We construct paired DT and culturally-adapted (CA) datasets via 1:1 seed matching for four languages - Korean (KO), Japanese (JA), Thai (TH), and Khmer (KM) - and compare Attack Success Rate (ASR) and Cultural Realism sco
The rapid deployment of LLMs globally has highlighted the critical shortcomings of English-centric safety evaluations, making culturally-adapted red-teaming an immediate necessity.
This research provides a methodology for developing culturally-sensitive safety benchmarks, crucial for the ethical and effective deployment of AI in diverse linguistic and social contexts, particularly in the Global South.
The understanding that direct translation of English benchmarks is insufficient for multilingual AI safety, requiring a more nuanced, culturally-adapted approach to red-teaming.
- · AI developers focused on ethical deployment
- · Users in East and Southeast Asian contexts
- · Countries developing AI governance frameworks
- · Linguistic and cultural research in AI
- · AI models with poor cultural adaptation
- · Developers relying solely on direct translation for safety
- · Homogenized global AI safety standards
- · Western-centric AI benchmark creators
Improved safety and reduced unintended biases in LLMs deployed in non-Western cultures.
Increased trust and adoption of AI technologies in culturally diverse regions, fostering local innovation.
The emergence of 'cultural AI safety' as a distinct and well-funded sub-field within AI research, demanding diverse interdisciplinary expertise.
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Read at arXiv cs.AI