Mitigating Hallucination in Large Language Models: Techniques, Applications, and Implications

Abstract

Large Language Models (LLMs) such as GPT, LLaMA, and PaLM have transformed the field of Natural Language Processing (NLP) by achieving remarkable results in text generation, summarization, translation, question answering, and dialogue systems. Their wide adoption across industries highlights their usefulness but also exposes a critical limitation—hallucination. Hallucination occurs when models generate information that is false, misleading, or fabricated. These errors can vary from small factual mistakes, like incorrect dates or figures, to serious inaccuracies that may cause harm in sensitive areas such as healthcare, education, and software development. This paper explores the concept and classification of hallucinations in LLMs, examines techniques to reduce them—including prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG)—and discusses ethical implications and real-world applications. By comparing multiple strategies, the study aims to contribute to developing more reliable and trustworthy AI systems.