URAG：在大学招生聊天机器人中实现统一混合 RAG 以提供精准答案 —— 以 HCMUT 为例的案例研究
URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT
AI总结：
本文提出统一混合 RAG（URAG）框架，通过整合特定数据提升大学招生聊天机器人的回答准确性，使轻量级模型媲美商业模型，并在 HCMUT 案例中验证了其有效性与实际部署可行性。
摘要：
随着人工智能特别是自然语言处理的快速发展，大语言模型（LLMs）已成为教育问答系统，尤其是大学招生聊天机器人的核心技术。检索增强生成（RAG）等先进技术被开发出来以增强这些系统，通过整合特定的大学数据，使 LLMs 能够就招生和学术咨询提供有依据的回答。然而，这些增强的 RAG 技术通常伴随着高昂的运营成本，并且需要训练复杂的专用模块，这给实际部署带来了挑战。此外，在教育场景中，提供准确的答案以防止错误信息至关重要，而基于 LLM 的系统在没有适当策略和方法的情况下很难胜任这一任务。本文提出了统一 RAG（URAG）框架，这是一种混合方法，可显著提高回答的准确性，尤其是针对关键查询。实验结果表明，URAG 增强了我们内部的轻量级模型，使其性能可与最先进的商业模型相媲美。此外，为了验证其实用性，我们在我们的教育机构进行了案例研究，并获得了积极的反馈和好评。本研究不仅证明了 URAG 的有效性，还突显了其在教育环境中实际部署的可行性。
英文摘要：
With the rapid advancement of Artificial Intelligence, particularly in Natural Language Processing, Large Language Models (LLMs) have become pivotal in educational question-answering systems, especially university admission chatbots. Concepts such as Retrieval-Augmented Generation (RAG) and other advanced techniques have been developed to enhance these systems by integrating specific university data, enabling LLMs to provide informed responses on admissions and academic counseling. However, these enhanced RAG techniques often involve high operational costs and require the training of complex, specialized modules, which poses challenges for practical deployment. Additionally, in the educational context, it is crucial to provide accurate answers to prevent misinformation, a task that LLM-based systems find challenging without appropriate strategies and methods. In this paper, we introduce the Unified RAG (URAG) Framework, a hybrid approach that significantly improves the accuracy of responses, particularly for critical queries. Experimental results demonstrate that URAG enhances our in-house, lightweight model to perform comparably to state-of-the-art commercial models. Moreover, to validate its practical applicability, we conducted a case study at our educational institution, which received positive feedback and acclaim. This study not only proves the effectiveness of URAG but also highlights its feasibility for real-world implementation in educational settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
