BURMESE-SAN: Burmese NLP Benchmark for Evaluating Large Language Models
arXiv:2602.18788v1 Announce Type: new 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 through a rigorous native-speaker-driven process to ensure linguistic naturalness, fluency, and cultural authenticity while minimizing translation-induced artifacts. We conduct a large-scale evaluation of both open-weight and commercial LLMs to examine challenges in Burmese modeling arising from limited pretraining coverage, rich morphology, and syntactic variation. Our results show that Burmese perfor
arXiv:2602.18788v1 Announce Type: new 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 through a rigorous native-speaker-driven process to ensure linguistic naturalness, fluency, and cultural authenticity while minimizing translation-induced artifacts. We conduct a large-scale evaluation of both open-weight and commercial LLMs to examine challenges in Burmese modeling arising from limited pretraining coverage, rich morphology, and syntactic variation. Our results show that Burmese performance depends more on architectural design, language representation, and instruction tuning than on model scale alone. In particular, Southeast Asia regional fine-tuning and newer model generations yield substantial gains. Finally, we release BURMESE-SAN as a public leaderboard to support systematic evaluation and sustained progress in Burmese and other low-resource languages. https://leaderboard.sea-lion.ai/detailed/MY
Executive Summary
The article introduces BURMESE-SAN, a comprehensive benchmark for evaluating large language models (LLMs) in Burmese, focusing on three core NLP competencies: understanding, reasoning, and generation. The benchmark includes seven subtasks, several of which were previously unavailable for Burmese, and is constructed through a rigorous process to ensure linguistic and cultural authenticity. The evaluation of both open-weight and commercial LLMs reveals that performance in Burmese is influenced more by architectural design, language representation, and instruction tuning than by model scale alone. The study highlights the challenges in Burmese modeling due to limited pretraining coverage, rich morphology, and syntactic variation. The authors release BURMESE-SAN as a public leaderboard to support ongoing evaluation and progress in Burmese and other low-resource languages.
Key Points
- ▸ Introduction of BURMESE-SAN as the first holistic benchmark for Burmese NLP.
- ▸ Evaluation of LLMs across understanding, reasoning, and generation competencies.
- ▸ Performance influenced by architectural design and language representation more than model scale.
- ▸ Release of BURMESE-SAN as a public leaderboard for sustained progress.
Merits
Comprehensive Benchmark
BURMESE-SAN provides a thorough evaluation of LLMs in Burmese, covering a wide range of NLP tasks that were previously unavailable.
Rigorous Construction
The benchmark is constructed through a native-speaker-driven process, ensuring linguistic naturalness, fluency, and cultural authenticity.
Insightful Findings
The study reveals important insights into the factors influencing Burmese LLM performance, such as architectural design and language representation.
Demerits
Limited Scope
While comprehensive, the benchmark may not cover all possible NLP tasks and challenges specific to Burmese.
Data Availability
The limited pretraining coverage for Burmese poses a significant challenge, which may affect the generalizability of the findings.
Expert Commentary
The introduction of BURMESE-SAN represents a significant advancement in the evaluation of LLMs for Burmese, addressing a critical gap in the field of NLP for low-resource languages. The benchmark's comprehensive coverage of core NLP competencies, coupled with its rigorous construction process, ensures that the evaluation is both thorough and culturally relevant. The study's findings, particularly the emphasis on architectural design and language representation over model scale, provide valuable insights for researchers and developers working on Burmese LLMs. The release of BURMESE-SAN as a public leaderboard is a commendable initiative that will support sustained progress in Burmese NLP and potentially inspire similar efforts for other low-resource languages. However, the limited pretraining coverage for Burmese remains a challenge that needs to be addressed to ensure the generalizability of the findings. Overall, this work sets a high standard for benchmark development and evaluation in the field of NLP.
Recommendations
- ✓ Increase investment in data collection and pretraining for Burmese to address the limited pretraining coverage.
- ✓ Encourage the development of similar benchmarks for other low-resource languages to promote equitable progress in NLP.