ViGPTQA - State-of-the-Art LLMs for Vietnamese Question Answering: System Overview, Core Models Training, and Evaluations

Minh Thuan Nguyen, Khanh Tung Tran, Nhu Van Nguyen, Xuan-Son Vu


Abstract
Large language models (LLMs) and their applications in low-resource languages (such as in Vietnamese) are limited due to lack of training data and benchmarking datasets. This paper introduces a practical real-world implementation of a question answering system for Vietnamese, called ViGPTQA, leveraging the power of LLM. Since there is no effective LLM in Vietnamese to date, we also propose, evaluate, and open-source an instruction-tuned LLM for Vietnamese, named ViGPT. ViGPT demonstrates exceptional performances, especially on real-world scenarios. We curate a new set of benchmark datasets that encompass both AI and human-generated data, providing a comprehensive evaluation framework for Vietnamese LLMs. By achieving state-of-the-art results and approaching other multilingual LLMs, our instruction-tuned LLM underscores the need for dedicated Vietnamese-specific LLMs. Our open-source model supports customized and privacy-fulfilled Vietnamese language processing systems.
Anthology ID:
2023.emnlp-industry.70
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2023
Address:
Singapore
Editors:
Mingxuan Wang, Imed Zitouni
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
754–764
Language:
URL:
https://aclanthology.org/2023.emnlp-industry.70
DOI:
10.18653/v1/2023.emnlp-industry.70
Bibkey:
Cite (ACL):
Minh Thuan Nguyen, Khanh Tung Tran, Nhu Van Nguyen, and Xuan-Son Vu. 2023. ViGPTQA - State-of-the-Art LLMs for Vietnamese Question Answering: System Overview, Core Models Training, and Evaluations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 754–764, Singapore. Association for Computational Linguistics.
Cite (Informal):
ViGPTQA - State-of-the-Art LLMs for Vietnamese Question Answering: System Overview, Core Models Training, and Evaluations (Nguyen et al., EMNLP 2023)
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