Large Knowledge Model: Perspectives and Challenges
Huajun Chen
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.