History, Development, and Principles of Large Language Models-An
  Introductory Survey
Zhibo Chu Shiwen Ni Zichong Wang Xi Feng Chengming Li Xiping Hu Ruifeng Xu Min Yang Wenbin Zhang
Language models serve as a cornerstone in natural language processing (NLP),
utilizing mathematical methods to generalize language laws and knowledge for
prediction and generation. Over extensive research spanning decades, language
modeling has progressed from initial statistical language models (SLMs) to the
contemporary landscape of large language models (LLMs). Notably, the swift
evolution of LLMs has reached the ability to process, understand, and generate
human-level text. Nevertheless, despite the significant advantages that LLMs
offer in improving both work and personal lives, the limited understanding
among general practitioners about the background and principles of these models
hampers their full potential. Notably, most LLMs reviews focus on specific
aspects and utilize specialized language, posing a challenge for practitioners
lacking relevant background knowledge. In light of this, this survey aims to
present a comprehensible overview of LLMs to assist a broader audience. It
strives to facilitate a comprehensive understanding by exploring the historical
background of language models and tracing their evolution over time. The survey
further investigates the factors influencing the development of LLMs,
emphasizing key contributions. Additionally, it concentrates on elucidating the
underlying principles of LLMs, equipping audiences with essential theoretical
knowledge. The survey also highlights the limitations of existing work and
points out promising future directions.