arXiv:2602.18788v3 Announce Type: replace Abstract: We introduce BURMESE-SAN, the first holistic benchmark that systematically evaluates large language models (LLMs) for Burmese across three core NLP competencies: understanding (NLU), reasoning (NLR), and generation (NLG). BURMESE-SAN consolidates seven subtasks spanning these competencies, including Question Answering, Sentiment Analysis, Toxicity Detection, Causal Reasoning, Natural Language Inference, Abstractive Summarization, and Machine Translation, several of which were previously unavailable for Burmese. The benchmark is constructed th
The proliferation of LLMs and increasing interest in their global application necessitates specialized benchmarks for languages beyond English, particularly for less-resourced languages like Burmese, to ensure equitable and responsible AI development.
This benchmark is crucial for advancing NLP capabilities in Burmese, fostering local AI development, and reducing dependency on models not optimized for cultural and linguistic nuances, which aligns with objectives of digital sovereignty.
The availability of BURMESE-SAN allows for systematic evaluation and improvement of LLMs for Burmese, which accelerates the development of more effective and appropriate AI applications for the region.
- · Myanmar's AI developers
- · Burmese language users
- · Multilingual LLM developers
- · NLP researchers
- · Monolingual Western LLMs
- · Generic translation services
Improved performance of Large Language Models in Burmese across NLU, NLR, and NLG tasks.
Increased investment and development of AI applications and infrastructure tailored for the Burmese language and Myanmar's digital economy.
Potential for Myanmar to establish greater digital autonomy and reduce reliance on foreign-developed AI stacks, fostering sovereign AI capabilities.
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