The recent advancements in the field of large language models (LLMs) have primarily focused on enhancing their reliability and accuracy, particularly in high-stakes applications such as healthcare, finance, and education. A significant trend has been the development of methods to mitigate hallucinations, which are factually incorrect or misleading outputs generated by LLMs. These methods include uncertainty quantification frameworks, novel calibration techniques, and the introduction of special tokens to express uncertainty or refusal. Additionally, there has been a notable shift towards integrating LLMs with external knowledge sources, such as knowledge graphs and retrieval-augmented generation systems, to improve the factual accuracy of their outputs. The field is also witnessing a growing interest in the ethical deployment of LLMs, with a focus on enabling models to refuse inappropriate requests and ensuring their responses are both accurate and trustworthy. Notably, some of the most innovative contributions include a 100% hallucination-free approach and a novel method for assessing and mitigating verb hallucinations in multimodal LLMs. These developments collectively aim to enhance the safety, reliability, and practicality of LLMs across various domains.