Vi-Mistral-X: Building a Vietnamese Language Model with Advanced Continual Pre-training
<|reference_start|>Vi-Mistral-X: Building a Vietnamese Language Model with Advanced Continual Pre-training: The advancement of Large Language Models (LLMs) has significantly transformed the field of natural language processing, although the focus on English-centric models has created a noticeable research gap for specific languages, including Vietnamese. To address this issue, this paper presents vi-mistral-x, an innovative Large Language Model designed expressly for the Vietnamese language. It utilizes a unique method of continual pre-training, based on the Mistral architecture, which incorporates grouped-query attention and sliding window attention techniques. This model, vi-Mistral-X, marks a significant step forward in improving the understanding and generation of the Vietnamese language. It introduces an additional phase of continual pre-training, specifically adapted for Vietnamese, enhancing the model's capability in understanding complex language nuances and generating accurate, context-aware Vietnamese text. Through comprehensive testing on various benchmarks, vi-mistral-x has shown to outperform existing Vietnamese LLMs in several key areas, including text classification, question answering, and text generation. Particularly, in the Vietnamese Multitask Language Understanding (VMLU) benchmark, vi-mistral-x sets a new standard, outperforming other available models significantly. This paper highlights the critical role of continual pre-training in advancing language-specific LLMs and opens new avenues for the development of multilingual models. We aim for vi-mistral-x to not just be an important asset for processing the Vietnamese language but also to encourage more advancements in creating large language models for languages that are less represented.<|reference_end|>
@article{vo2024vi-mistral-x:,
    title={Vi-Mistral-X: Building a Vietnamese Language Model with Advanced
  Continual Pre-training},
    author={James Vo},
    journal={arXiv preprint arXiv:2403.15470},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15470},
    primaryClass={cs.CL}
}