Large Knowledge Model: Perspectives and Challenges

Humankind's understanding of the world is fundamentally linked to our perception and cogni- tion, with human languages serving as one of the major carriers of world knowledge. In this vein, Large Language Models (LLMs) like ChatGPT epitomize the pre-training of extensive, sequence-based world knowledge into neural networks, facilitating the processing and manipu- lation 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, se- mantic tools for LLM and knowledgeable AI agents. Subsequently, we examine how LLMs can boost traditional symbolic knowledge bases, encompassing aspects like using LLM as KG builder and controller, structured knowledge pretraining, and LLM-enhanced symbolic reason- ing. Considering the intricate nature of human knowledge, we advocate for the creation of Large Knowledge Models (LKM), specifically engineered to manage diversified spectrum of knowl- edge structures. This promising undertaking would entail several key challenges, such as dis- entangling knowledge base from language models, cognitive alignment with human knowledge, integration of perception and cognition, and building large commonsense models for interacting with physical world, among others. We finally propose a five-"A" principle to distinguish the concept of LKM.