This article provides a comprehensive survey of Large Language Models (LLMs), covering their evolution from early neural models to modern architectures like GPT, LLaMA, and PaLM. It details the technical processes of building LLMs, including data cleaning, tokenization, and training strategies, and explores their applications, limitations, and enhancement techniques such as RAG and prompt engineering. The review also examines popular datasets, evaluation benchmarks, and future research directions, serving as a valuable resource for understanding the current state and potential of LLMs.
原文翻译：
本文对大语言模型（LLMs）进行了全面综述，涵盖从早期神经模型到现代架构（如GPT、LLaMA和PaLM）的演进。详细阐述了构建LLMs的技术流程，包括数据清洗、标记化和训练策略，并探讨了其应用、局限性以及增强技术，如RAG和提示工程。该综述还考察了流行数据集、评估基准和未来研究方向，为理解LLMs的现状和潜力提供了宝贵资源。This article provides a comprehensive survey of Large Language Models (LLMs), covering their evolution from early neural models to modern architectures like GPT, LLaMA, and PaLM. It details the technical processes of building LLMs, including data cleaning, tokenization, and training strategies, and explores their applications, limitations, and enhancement techniques such as RAG and prompt engineering. The review also examines popular datasets, evaluation benchmarks, and future research directions, serving as a valuable resource for understanding the current state and potential of LLMs.
原文翻译：
本文对大语言模型（LLMs）进行了全面综述，涵盖从早期神经模型到现代架构（如GPT、LLaMA和PaLM）的演进。详细阐述了构建LLMs的技术流程，包括数据清洗、标记化和训练策略，并探讨了其应用、局限性以及增强技术，如RAG和提示工程。该综述还考察了流行数据集、评估基准和未来研究方向，为理解LLMs的现状和潜力提供了宝贵资源。
