Bridging the Multilingual Safety Divide: Efficient, Culturally-Aware Alignment for Global South Languages

• URL: http://arxiv.org/abs/2602.13867v1
• Date: Sat, 14 Feb 2026 19:56:40 GMT
• Title: Bridging the Multilingual Safety Divide: Efficient, Culturally-Aware Alignment for Global South Languages
• Authors: Somnath Banerjee, Rima Hazra, Animesh Mukherjee,
• Abstract summary: Large language models (LLMs) are being deployed across the Global South.<n> Everyday use involves low-resource languages, code-mixing, and culturally specific norms.<n>Our aim is to make multilingual safety a core requirement-not an add-on-for equitable AI in underrepresented regions.
• Score: 8.667909336164465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are being deployed across the Global South, where everyday use involves low-resource languages, code-mixing, and culturally specific norms. Yet safety pipelines, benchmarks, and alignment still largely target English and a handful of high-resource languages, implicitly assuming safety and factuality ''transfer'' across languages. Evidence increasingly shows they do not. We synthesize recent findings indicating that (i) safety guardrails weaken sharply on low-resource and code-mixed inputs, (ii) culturally harmful behavior can persist even when standard toxicity scores look acceptable, and (iii) English-only knowledge edits and safety patches often fail to carry over to low-resource languages. In response, we outline a practical agenda for researchers and students in the Global South: parameter-efficient safety steering, culturally grounded evaluation and preference data, and participatory workflows that empower local communities to define and mitigate harm. Our aim is to make multilingual safety a core requirement-not an add-on-for equitable AI in underrepresented regions.

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