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A Small Language Model and Domain-Specific Resources for Vietnamese Public Services

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Van Thai Le
Anh-Cuong Le
Published in
Multi-disciplinary Trends in Artificial Intelligence
Publisher
Springer Nature Singapore

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Abstract

This chapter introduces VietPSLM, a compact language model designed to enhance question-answering capabilities in Vietnamese public services. The model addresses the challenges of formal language, scattered documentation, and lack of interactive support in e-government systems. Key topics include the construction of domain-specific resources such as VietGovCorpus and VietAdminQA, the use of efficient domain adaptation strategies like LoRA fine-tuning and quantization, and the implementation of a two-pass inference mechanism for answer refinement. The model achieves competitive performance with proprietary models while remaining efficient and open-weight. The chapter also discusses the evaluation benchmarks ViPS-EvalGen and ViPS-MCBench, and the methodology involving continued pretraining, supervised fine-tuning, and inference refinement. The results demonstrate significant improvements in generative and multiple-choice QA tasks, making VietPSLM a promising solution for improving accessibility and efficiency in Vietnamese public services.
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previous chapter Solving the Student-Project Allocation Problem with Preferences over Projects Using a Multi-start Local Search
next chapter Multimodal Tree Crown Detection and Carbon Stock Estimation from Remote Sensing Imagery
Title
A Small Language Model and Domain-Specific Resources for Vietnamese Public Services
Authors
Van Thai Le
Anh-Cuong Le
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Multi-disciplinary Trends in Artificial Intelligence

Print ISBN: 978-981-9549-59-7

Electronic ISBN: 978-981-9549-60-3

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-4960-3_2
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