Humankind's understanding of the world is fundamentally linked to our perception and cognition, with \emph{human languages} serving as one of the major carriers of \emph{world knowledge}. In this vein, \emph{Large Language Models} (LLMs) like ChatGPT epitomize the pre-training of extensive, sequence-based world knowledge into neural networks, facilitating the processing and manipulation of this knowledge in a parametric space. This article explores large models through the lens of ``knowledge''. We initially investigate the role of symbolic knowledge such as Knowledge Graphs (KGs) in enhancing LLMs, covering aspects like knowledge-augmented language model, structure-inducing pre-training, knowledgeable prompts, structured CoT, knowledge editing, semantic tools for LLM and knowledgeable AI agents. Subsequently, we examine how LLMs can amplify traditional symbolic knowledge bases, encompassing aspects like using LLM as KG builder and controller, structured knowledge pretraining, LLM-enhanced symbolic reasoning, and the amalgamation of perception with cognition. Considering the intricate nature of human knowledge, we advocate for the creation of \emph{Large Knowledge Models} (LKM), specifically engineered to manage diversified spectrum of knowledge structures. This ambitious undertaking could entail several key challenges, such as disentangling knowledge representation from language models, restructuring pre-training with structured knowledge, and building large commonsense models, among others. We finally propose a five-``A'' principle to distinguish the concept of LKM.

翻译：人类对世界的理解从根本上与我们的感知和认知紧密相连，其中**人类语言**作为**世界知识**的主要载体之一。在此背景下，像ChatGPT这样的**大型语言模型**（LLMs）体现了将大规模的、基于序列的世界知识预训练到神经网络中，从而在参数空间中促进这些知识的处理与操作。本文从“知识”的视角审视大型模型。我们首先研究符号知识（如知识图谱KG）在增强LLM中的作用，涵盖知识增强型语言模型、结构诱导预训练、知识引导提示、结构化思维链、知识编辑、LLM的语义工具以及知识型AI智能体等方面。随后，我们探讨LLM如何放大传统符号知识库，包括将LLM用作KG构建器与控制器、结构化知识预训练、LLM增强的符号推理，以及感知与认知的融合。考虑到人类知识的复杂性，我们倡导构建**大型知识模型**（LKM），专门用于管理多样化的知识结构体系。这一雄心勃勃的工程可能涉及若干关键挑战，例如从语言模型中解耦知识表示、使用结构化知识重构预训练，以及构建大型常识模型等。最后，我们提出五“A”原则来界定LKM的概念。