Large Language Models （LLMs） are a class of language models composed of artificial neural networks with a vast number of parameters （typically billions of weights or more）. They are trained on a large amount of unlabeled text using self-supervised or semi-supervised learning and are the core of current generative Artificial Intelligence （AI） technologies. Compared to traditional language models， LLMs demonstrate stronger language understanding and generation capabilities， supported by substantial computational power， extensive parameters， and large-scale data. They are widely applied in tasks such as machine translation， question answering systems， and dialogue generation with good performance. Most of the existing surveys focus on the theoretical construction and training techniques of LLMs， while systematic exploration of LLMs’ industry-level application practices and evolution of the technological ecosystem remains insufficient. Therefore， based on introducing the foundational architecture， training techniques， and development history of LLMs， the current general key technologies in LLMs and advanced integration technologies with LLMs bases were analyzed. Then， by summarizing the existing research， challenges faced by LLMs in practical applications were further elaborated， including problems such as data bias， model hallucination， and computational resource consumption， and an outlook was provided on the ongoing development trends of LLMs.
