用检索增强生成技术打造精准学术助手，帮学生查项目规定
Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation: An evaluation study for bachelor projects
- 用检索增强生成融合实时校规数据，提升回答准确性
- 实测显示助手能有效解答学生在项目中的具体问题
- 适合教育类AI系统研发者参考，尤其关注可靠性
大型语言模型因其生成类人文本和处理复杂查询的能力，被广泛用于虚拟助手开发。然而，在高度专业的内容领域，模型仍存在幻觉、信息缺失及难以提供准确上下文响应等问题。本文针对马斯特里赫特大学学生在项目执行中遇到的规则查询需求，构建了一个基于检索增强生成（Retrieval-Augmented Generation）的虚拟助手，通过整合最新、领域特定的知识，提升响应的准确性和可靠性。借助严格的评估框架与真实场景测试，结果表明该助手能有效满足学生需求，并应对大模型在专业教育场景中的固有挑战。本研究为改进特定应用下的大模型系统提供了实践支持，同时指出了未来研究方向。
原文摘要 · Abstract (English)
Large Language Models have been increasingly employed in the creation of Virtual Assistants due to their ability to generate human-like text and handle complex inquiries. While these models hold great promise, challenges such as hallucinations, missing information, and the difficulty of providing accurate and context-specific responses persist, particularly when applied to highly specialized content domains. In this paper, we focus on addressing these challenges by developing a virtual assistant designed to support students at Maastricht University in navigating project-specific regulations. We propose a virtual assistant based on a Retrieval-Augmented Generation system that enhances the accuracy and reliability of responses by integrating up-to-date, domain-specific knowledge. Through a robust evaluation framework and real-life testing, we demonstrate that our virtual assistant can effectively meet the needs of students while addressing the inherent challenges of applying Large Language Models to a specialized educational context. This work contributes to the ongoing discourse on improving LLM-based systems for specific applications and highlights areas for further research.
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