With the rapid emergence of novel capabilities in Large Language Models (LLMs), the need for rigorous multilingual and multiculturalbenchmarks that are integrated has become more pronounced. Though existing LLM benchmarks are capable of evaluating specificcapabilities of LLMs in English as well as in various mid- to low-resource languages, including those in the Southeast Asian (SEA)region, a comprehensive and culturally representative evaluation suite for the SEA languages has not been developed thus far.Here, we present SEA-HELM, a holistic linguistic and cultural LLM evaluation suite that emphasises SEA languages, comprisingfive core pillars: (1) NLP CLASSICS, (2) LLM-SPECIFICS, (3) SEA LINGUISTICS, (4) SEA CULTURE, (5) SAFETY. SEA-HELMcurrently supports Filipino, Indonesian, Tamil, Thai, and Vietnamese. We also introduce the SEA-HELM leaderboard, which allows users to understand models’ multilingual and multicultural performance in a systematic and user-friendly manner. We make the SEA-HELM evaluation code publicly available.

Recently, Large Language Models (LLMs) have dominated much of the artificial intelligence scene with their ability to process and generate natural languages. However, the majority of LLM research and development remains English-centric, leaving low-resource languages such as those in the Southeast Asian (SEA) region under-represented. To address this representation gap, we introduce Llama-SEA-LION-v3-8B-IT and Gemma-SEA-LION-v3-9B-IT, two cutting-edge multilingual LLMs designed for SEA languages. The SEA-LION family of LLMs supports 11 SEA languages, namely English, Chinese, Indonesian, Vietnamese, Malay, Thai, Burmese, Lao, Filipino, Tamil, and Khmer. Our work leverages large-scale multilingual continued pre-training with a comprehensive post-training regime involving multiple stages of instruction fine-tuning, alignment, and model merging. Evaluation results on multilingual benchmarks indicate that our models achieve state-of-the-art performance across LLMs supporting SEA languages. We open-source the models to benefit the wider SEA community.