Integrating Large Language Models and Knowledge Graphs for Next-level AGI

Large language models (LLMs), due to their emergent ability and generalizability, are making new waves in developing Artificial General Intelligence (AGI). However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, Knowledge Graphs (KGs) are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolve by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to integrating LLMs and KGs together and simultaneously leveraging their advantages to achieve AGI's ultimate goal: to reason, adapt, and synthesize knowledge with human-level nuance and factual accuracy.