SEA-HELM

SEA-HELM is a benchmark rebuilt on the Stanford HELM (Holistic Evaluation of Language Models) framework, retargeted at Southeast Asian languages.

Evaluation dimensions include:

• NLU tasks: text classification, question answering, reading comprehension, natural language inference
• NLG tasks: summarisation, translation, dialogue generation
• Linguistic competence: grammar, semantics, lexical knowledge
• World knowledge: Southeast Asian culture, history, geography
• Safety: bias, harmful content, misleading outputs
• Multilingual capability: cross-lingual transfer, code-switching

The 11 supported languages: English, Chinese, Malay, Indonesian, Thai, Vietnamese, Filipino, Tamil, Burmese, Khmer, Lao.

The leaderboard is open at leaderboard.sea-lion.ai and runs comparative testing across global LLMs (GPT-4, Claude, Gemini, Llama, Qwen, SEA-LION, etc.).

SEA-HELM tackles a badly underestimated problem: Southeast Asian language LLMs had no fair evaluation.

Earlier global benchmarks (MMLU, HellaSwag, HumanEval, etc.) are almost entirely English, with a sprinkle of Chinese/French/German. Southeast Asian languages — especially Tamil, Burmese, Khmer and others — were barely covered in mainstream benchmarks. The consequences:

• General-purpose LLM vendors had no way to demonstrate capability in these languages
• Local Southeast Asian LLM vendors could not be assessed objectively
• Academic progress on these languages could not be quantified

SEA-HELM offers, for the first time, a unified, public, reproducible evaluation, so every LLM can be benchmarked against the others on Southeast Asian languages. The results were surprising:

• GPT-4 / Claude perform decently on Thai and Vietnamese but collapse on Burmese, Khmer, and Lao
• SEA-LION v3 overtakes GPT-4 on smaller languages, proving the continued pre-training strategy works
• Open-source models like Llama and Gemma are inconsistent across Southeast Asian languages

This data has become the most important "hard evidence" for SEA-LION's commercialisation.

SEA-HELM and SEA-LION are a pair — without evaluation, there is no credibility for SEA-LION's commercialisation.

In the seven-lever framework:

• Lever 6 (international): SEA-HELM gives Singapore a voice on "regional language capability assessment" in ASEAN AI cooperation
• Lever 3 (industry adoption): local enterprises can use SEA-HELM to pick the right LLM for their needs
• Lever 4 (governance): evaluation results provide an objective basis for government LLM procurement

A take: SEA-HELM is a critical step in the "standards battle" within Singapore's AI strategy. It is not a product, but it defines "what counts as a good Southeast Asian LLM" — and that definitional power is more durable than any single model. Even if SEA-LION is eventually surpassed by other models, SEA-HELM remains; as long as Southeast Asian LLMs need to be evaluated, Singapore sits on the standard.

Worth watching: how quickly SEA-HELM updates (GenAI moves fast and benchmarks go stale easily), integration with global benchmarks (whether HELM, Big-Bench, and the HuggingFace OpenLLM leaderboard recognise SEA-HELM), and methodological controversies (dataset quality for smaller languages, statistical reliability of the evaluations).