SeaLLMs -- Large Language Models for Southeast Asia
Xuan-Phi Nguyen Wenxuan Zhang Xin Li Mahani Aljunied Qingyu Tan Liying Cheng Guanzheng Chen Yue Deng Sen Yang Chaoqun Liu Hang Zhang Lidong Bing
Despite the remarkable achievements of large language models (LLMs) in
various tasks, there remains a linguistic bias that favors high-resource
languages, such as English, often at the expense of low-resource and regional
languages. To address this imbalance, we introduce SeaLLMs, an innovative
series of language models that specifically focuses on Southeast Asian (SEA)
languages. SeaLLMs are built upon the Llama-2 model and further advanced
through continued pre-training with an extended vocabulary, specialized
instruction and alignment tuning to better capture the intricacies of regional
languages. This allows them to respect and reflect local cultural norms,
customs, stylistic preferences, and legal considerations. Our comprehensive
evaluation demonstrates that SeaLLM-13b models exhibit superior performance
across a wide spectrum of linguistic tasks and assistant-style
instruction-following capabilities relative to comparable open-source models.
Moreover, they outperform ChatGPT-3.5 in non-Latin languages, such as Thai,
Khmer, Lao, and Burmese, by large margins while remaining lightweight and
cost-effective to operate.