Using Large Language Models for education managements in Vietnamese with low resources

TL;DR

This paper introduces VietEduFrame, a framework that adapts large language models for educational management in Vietnamese institutions with limited resources, using a specialized dataset and demonstrating improved accuracy and efficiency.

Contribution

The paper presents a tailored dataset and a framework for applying LLMs to Vietnamese educational management, addressing resource constraints and enhancing performance.

Findings

Outperforms existing methods in accuracy and efficiency

Utilizes synthetic data to supplement real-world examples

Offers a promising solution for under-resourced educational environments

Abstract

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.