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.

人类对世界的理解与我们的感知和认知密切相关，其中人类语言作为世界知识的重要承载者之一。本文通过 “知识” 这个视角来探索大型模型，并讨论符号知识如何增强大型语言模型以及大型语言模型如何扩展传统符号知识基础。考虑到人类知识的复杂性，我们倡导创建专门管理多样化知识结构的大型知识模型，并提出了五个 “A” 原则来区分 LKM 的概念。