Mitigating Hallucinations in Large Language Models for Educational Application

Large Language Models (LLMs) have transformed various fields, including education, by providing intelligent and automated text generation. However, the tendency of these models to generate hallucinated or factually incorrect information poses significant challenges, especially in educational contexts where ac-curacy is paramount. This paper explores various methodologies to mitigate hallucinations in LLMs, focusing on their application in educational settings. We review current approaches, propose a multi-stage framework for reducing hallucinations, and evaluate its effectiveness through empirical studies.