arXiv:2606.03284v1 Announce Type: new Abstract: Frontier LLMs perform well in Western contexts, but remain poorly tested on underrepresented cultures such as those in Southeast Asia (SEA). Existing NLI benchmarks are largely Western-centric, translation-derived, or monolingual, limiting their ability to measure culturally grounded reasoning. We introduce SEA-NLI, a native, culturally grounded NLI benchmark covering eight SEA countries in English and native regional languages, verified by native speakers. Across 17 encoder and decoder models, we observe a low performance from all models, especi
The rapid deployment of frontier LLMs globally highlights their Western bias, creating an urgent need for culturally relevant benchmarks in underrepresented regions like Southeast Asia.
This development exposes significant limitations in current AI models for diverse cultural contexts, indicating a forthcoming push for more inclusive and regionally specific AI development.
The focus of AI development and evaluation will broaden beyond Western-centric criteria, leading to the creation of more diverse datasets and models capable of understanding non-Western cultures.
- · Southeast Asian language AI developers
- · Multilingual LLM researchers
- · Organizations operating in Southeast Asia
- · Computational linguistics
- · Unadapted Western-centric LLMs
- · Developers ignoring cultural nuance in AI
- · Monolingual AI development approaches
AI models will perform poorly in Southeast Asian contexts without significant adaptation, affecting adoption and utility.
Increased investment in building local AI talent and data infrastructure will emerge in Southeast Asian nations to address this gap.
This could accelerate the development of 'sovereign AI' initiatives in SEA regions, fostering technological independence and cultural preservation through AI.
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