@inproceedings{32eb7eda43d648999d7721d16704966d,
title = "Dynamic Multi-view RAG: Mitigating Hallucinations of Large Language Models in Education",
abstract = "Nowadays, LLMs are increasingly used in education. However, hallucination severely undermines the reliability and pedagogical value of their responses. While Retrieval-Augmented Generation (RAG) helps to mitigate hallucinations with external knowledge, existing methods not only rely on limited retrieval sources, but also lack mechanisms to verify whether generated answers are sufficiently supported by retrieved evidence. To address these limitations, we propose a Dynamic Multi-View RAG (DMVR) framework. First, DMVR dynamically rewrites each original query from textbook and example perspectives to perform multi-view retrieval. In addition, a verification mechanism generation process encourages cross-checking across multi-view sources and explicitly grounds answers in supporting evidence. Experiments on question answering tasks show that DMVR outperforms baselines in answer accuracy, showing its effectiveness in mitigating hallucinations.",
keywords = "Dynamic Multi-View Retrieval, Education, Hallucination, LLMs, RAG",
author = "Weijun Zhao and Qiwen Dong",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 17th BenchCouncil International Symposium on Benchmarking, Measuring, and Optimizing, Bench 2025 ; Conference date: 03-12-2025 Through 05-12-2025",
year = "2026",
doi = "10.1007/978-981-95-9694-2\_5",
language = "英语",
isbn = "9789819596935",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "54--65",
editor = "Jianfeng Zhan and Fanda Fan and Wei Wang and Yushan Su",
booktitle = "Evaluation Science and Engineering - 17th BenchCouncil International Symposium, Bench 2025, Revised Selected Papers",
address = "德国",
}