Research on a China-Laos Government Service System Based on Large Language Models

Abstract

: Under the framework of the Belt and Road Initiative, cross-border cooperation between China and Laos continues to deepen, leading to a growing demand for efficient governmental communication. However, the significant linguistic and cultural differences between Chinese and Lao constitute a major barrier to accurate information exchange. Traditional machine translation methods struggle to effectively handle the specialized terminology and complex contextual nuances inherent in governmental texts. To address this challenge, this paper proposes an intelligent China-Laos cross-border government service system based on Large Language Models (LLMs). The system integrates multilingual text processing with a domain-specific knowledge graph to achieve precise cross-lingual information retrieval and intelligent question answering. By leveraging advanced techniques such as multi-task learning, transfer learning, and the fusion of LLMs with Graph Neural Networks (GNNs), the system achieves high accuracy in semantic understanding and generation. This work aims to enhance communication efficiency and information accuracy between governmental departments and citizens of both countries, thereby supporting the high-quality development of cross-border public services.