  Large Language Models (LLMs) have shown remarkable abilities across various
tasks, yet their development has predominantly centered on high-resource
languages like English and Chinese, leaving low-resource languages underserved.
To address this disparity, we present SeaLLMs 3, the latest iteration of the
SeaLLMs model family, tailored for Southeast Asian languages. This region,
characterized by its rich linguistic diversity, has lacked adequate language
technology support. SeaLLMs 3 aims to bridge this gap by covering a
comprehensive range of languages spoken in this region, including English,
Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao,
Tamil, and Javanese. Leveraging efficient language enhancement techniques and a
specially constructed instruction tuning dataset, SeaLLMs 3 significantly
reduces training costs while maintaining high performance and versatility. Our
model excels in tasks such as world knowledge, mathematical reasoning,
translation, and instruction following, achieving state-of-the-art performance
among similarly sized models. Additionally, we prioritized safety and
reliability by addressing both general and culture-specific considerations and
incorporated mechanisms to reduce hallucinations. This work underscores the
importance of inclusive AI, showing that advanced LLM capabilities can benefit
underserved linguistic and cultural communities.
