Mitigating Hallucinations in Large Language Models
Dr. Vikas Joshi1*, Dr. Pallavi Krishna Purohit2
Abstract
Large language models have become central to modern natural language processing because they can generate fluent, context-aware, and useful text across a wide range of tasks. However, their tendency to produce hallucinated content remains one of the biggest barriers to safe and trustworthy deployment. Hallucination can take the form of fabricated facts, unsupported claims, or confident statements that are not grounded in evidence. This paper reviews the causes of hallucination in large language models and examines the most widely used mitigation strategies, including prompt engineering, retrieval-augmented generation, verification pipelines, reasoning-based refinement, and model alignment. The discussion shows that no single technique fully eliminates hallucination; instead, systems that combine retrieval, reasoning, and post-generation checking tend to perform more reliably. The paper also highlights important implementation challenges such as latency, retrieval noise, evaluation difficulty, and the trade-off between factuality and usability. Finally, future research directions are outlined for building more transparent, reliable, and trustworthy language models.
Keywords:
Large language models, hallucination, factuality, retrieval-augmented generation, verification, prompt engineering, alignment
