The field of large language models is witnessing significant developments in addressing the challenge of hallucinations, which refers to the generation of non-factual or misleading content. Researchers are exploring innovative approaches to mitigate hallucinations, including the use of linguistic nuances, bounded input perturbations, and noise augmented fine-tuning. These methods aim to enhance the factual accuracy and robustness of large language models, making them more reliable and trustworthy. Noteworthy papers in this regard include The Illusionist's Prompt, which introduces a novel hallucination attack that incorporates linguistic nuances into adversarial queries, and Noise-Augmented Fine-Tuning, which leverages adaptive noise injection to enhance model robustness. Other notable works include TARAC, which proposes a temporal attention real-time accumulative connection method to mitigate hallucinations in large vision-language models, and HalluciNot, which presents a comprehensive system for detecting hallucinations in large language model outputs through context and common knowledge verification.