The extensive proliferation of large language models has profoundly influenced the domain of natural language processing, facilitating the creation of chatbots proficient in contextually managing a wide array of sentences. Nonetheless, a paramount challenge afflicting these models is the occurrence of hallucination, where inaccurate or fictitious data is produced. This comprehensive survey investigates the methodologies for identifying and alleviating hallucinations within large language models (LLMs) through the utilization of contextual indicators and embedded markers. Employing metrics such as the F1-score, Micro-Hallucination Rate (MiHR), and Macro-Hallucination Rate (MaHR) offers quantitative evaluations of the frequency of hallucinations in model-generated outputs. Additionally, the survey delves into the consequences of misinformation dissemination within LLMs and emphasizes the crucial importance of metacognitive strategies in guaranteeing the dependability of text generation while minimizing alignment tax risks. The discourse further extends to the mitigation of hallucinations in the realm of large multimodal models, highlighting the essential need for interdisciplinary collaboration to reinforce AI systems and underscoring the imperative to advance diverse and reliable AI technologies.