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Background

  • Background The paper discusses the outstanding capabilities of Large Language Models (LLMs) but highlights the fact that their development has mostly focused on high-resource languages like English and Chinese, leaving low-resource languages with inadequate services. The paper introduces SeaLLMs 3, the latest iteration of the SeaLLMs model family, designed specifically for Southeast Asian languages, a region marked by rich linguistic diversity but lacking sufficient language technology support.

  • Existing Work Prior models like SEA-LION and Sailor face considerable limitations: they are usually released only as foundational or chat models, have limited model size options, and cover a restricted number of SEA languages. Additionally, the relatively scarce availability of language corpora further restricts the development and performance of these models.

Core Contributions

  • Introduced a Better Model: SeaLLMs 3

    • Challenge 1: Need for Covering More Languages SeaLLMs 3 aims to cover a broad range of Southeast Asian languages, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese. The authors employed efficient language enhancement techniques and a specially constructed instruction tuning dataset to significantly reduce training costs while maintaining high performance and versatility.

    • Challenge 2: Enhancing Model Security and Reliability In multilingual settings, model reliability and trust are often overlooked. SeaLLMs 3 pays special attention to these issues, employing various mechanisms to reduce hallucination and ensuring that the model provides culturally appropriate responses. Moreover, the study introduces a new benchmark, SeaRefuse, to assess the LLM's capability to recognize its knowledge boundaries.

Implementation and Deployment

SeaLLMs 3 features innovative implementations and applications; it uses corpora integrated from Wikipedia and diverse sources and enhances regional knowledge through model-synthetic data. Language-specific neuron training has significantly reduced overall training costs and demonstrated state-of-the-art performance in multiple tasks. Furthermore, the development process of SeaLLMs 3 has emphasized model trustworthiness and reliability, utilizing special training to reduce hallucinations. The paper also mentions the open sourcing of the SeaLLMs 3 model, indicating that the foundational model can be tailored to specific application requirements.

Summary

SeaLLMs 3 is a Large Language Model designed for the multilingual environment of Southeast Asia, focusing on overcoming the limitations of existing models by employing efficient language enhancement techniques and safety and reliability mechanisms to provide culturally appropriate responses while minimizing hallucination phenomena.