SEATauBench is the first agent benchmark for SEA languages, finding that performance holds for language-only changes but degrades sharply with full domain localization.
Sea-helm: Southeast asian holistic evaluation of language models
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Language models degrade over 300 times in performance on Romanized Sinhala versus Unicode, with model size showing no correlation to script robustness.
JT-Safe-V2 is a safety-by-design LLM that reports SOTA scores on both capability and safety benchmarks while Safe-MoMA cuts inference cost over 30 percent.
Recursive character-based chunking at 300 characters outperforms Sentence-Based, Khmer-Aware, and LLM-Based methods on L2 distance, answer relevance, and Khmer IoU in a 5-fold evaluation on 18 Khmer agricultural QA pairs.
citing papers explorer
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SEATauBench: Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages
SEATauBench is the first agent benchmark for SEA languages, finding that performance holds for language-only changes but degrades sharply with full domain localization.
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Script Sensitivity: Benchmarking Language Models on Unicode, Romanized and Mixed-Script Sinhala
Language models degrade over 300 times in performance on Romanized Sinhala versus Unicode, with model size showing no correlation to script robustness.
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JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data
JT-Safe-V2 is a safety-by-design LLM that reports SOTA scores on both capability and safety benchmarks while Safe-MoMA cuts inference cost over 30 percent.
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Evaluation of Chunking Strategies for Effective Text Embedding in Low-Resource Language on Agricultural Documents
Recursive character-based chunking at 300 characters outperforms Sentence-Based, Khmer-Aware, and LLM-Based methods on L2 distance, answer relevance, and Khmer IoU in a 5-fold evaluation on 18 Khmer agricultural QA pairs.