用大模型提升越南教育管理效率，适配资源匮乏场景。
Using Large Language Models for education managements in Vietnamese with low resources
- 构建专用于越南教育管理的框架与数据集
- 在有限资源下实现更高准确率与效率
- 适合教育信息化基础薄弱地区使用
自2022年ChatGPT发布以来，大型语言模型（如GPT-4、Gemini 1.5、Claude 3.5 Sonnet和Llama3）在各类自然语言处理任务中展现出显著进展。然而，在资源受限环境中，微调与部署大模型仍存在计算成本高的问题。本文提出VietEduFrame框架，专为越南教育机构的管理任务设计。核心贡献包括基于河内VNU学生教育文档构建的定制化数据集，解决了资源匮乏教育系统面临的独特挑战。通过大量实验表明，该方法在准确率与效率上均优于现有方案，为资源不足环境下的教育管理优化提供了可行路径。尽管框架利用合成数据补充真实案例，未来仍需关注其泛化能力与鲁棒性。
原文摘要 · Abstract (English)
Large language models (LLMs), such as GPT-4, Gemini 1.5, Claude 3.5 Sonnet, and Llama3, have demonstrated significant advancements in various NLP tasks since the release of ChatGPT in 2022. Despite their success, fine-tuning and deploying LLMs remain computationally expensive, especially in resource-constrained environments. In this paper, we proposed VietEduFrame, a framework specifically designed to apply LLMs to educational management tasks in Vietnamese institutions. Our key contribution includes the development of a tailored dataset, derived from student education documents at Hanoi VNU, which addresses the unique challenges faced by educational systems with limited resources. Through extensive experiments, we show that our approach outperforms existing methods in terms of accuracy and efficiency, offering a promising solution for improving educational management in under-resourced environments. While our framework leverages synthetic data to supplement real-world examples, we discuss potential limitations regarding broader applicability and robustness in future implementations.
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