To reduce the limitation of the large language model, the existing work enhances pre-trained LLMs using grounded knowledge.

For example, retrieval-augmented generation, remains an open question, knowledge graphs (KG).

In this paper, they propose graph neural prompting (GNP), a plug-and-play method to help pre-trained LLMs gain beneficial knowledge from KGs.

Powerpoint for this talk

Reference Paper

• Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
• Deep Bidirectional Language-Knowledge Graph Pretraining