Abstract: Over the past two decades, language modeling (LM) has emerged as a primary methodology for language understanding and generation. This technology has become a cornerstone within the field of natural language processing (NLP). At its core, LM is designed to train models to predict the probability of the next word or token, thereby generating natural and fluent language. The advent of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers and GPT-3, marks a significant milestone in the evolution of LM. These LLMs have left a profound impact on the field of artificial intelligence (AI) while also paving the way for advancements in other domains. This progression underscores the power and efficacy of AI, illustrating how the landscape of AI research has been reshaped by the rapid advancement of LLMs. This paper provides a comprehensive review of the evolution of LLMs, focusing on the technical architecture, model scale, training methods, optimization techniques, and evaluation metrics. Language models have evolved significantly over time, starting from initial statistical language models, moving onto neural network-based models, and now embracing the era of advanced pre-trained language models. As the scale of these models has expanded, so has their performance in language understanding and generation. This has led to notable results across various sectors, including education, healthcare, finance, and industry. However, the application of LLMs also presents certain challenges, such as data quality, model generalization capabilities, and computational resources. This paper delves into these issues, providing an analysis of the strengths and limitations of LLMs. Furthermore, the rise of LLMs has sparked a series of ethical, privacy, and security concerns. For instance, LLMs may generate discriminatory, false, or misleading information, infringe on personal privacy, or even be exploited for malicious activities such as cyber-attacks. To tackle these issues, this paper explores relevant technical measures, such as model interpretability, privacy protection, and security assessments. Ultimately, the paper outlines potential future research trends of LLMs. With ongoing enhancements to model scale and efficiency, LLMs are expected to play an even greater role in multimodal processing and societal impact. For example, by integrating information from different modalities, such as images and sound, LLMs can better understand and generate language. Additionally, they can be employed for societal impact assessment, providing support for policy formulation and decision-making. By thoroughly analyzing the current state of research and potential future directions, this paper aims to offer researchers valuable insights and inspiration regarding LLMs, thereby fostering further advancement in the field.