Chain of Knowledge: A Framework for Grounding Large Language Models with Structured Knowledge Bases

Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Lidong Bing, Shafiq Joty, Soujanya Poria

We introduce Chain of Knowledge (CoK), a framework that augments large language models with structured knowledge bases to improve factual correctness and reduce hallucination. Compared to previous works which only retrieve unstructured texts, CoK leverages structured knowledge bases which support complex queries and offer more direct factual statements. To assist large language models to effectively query knowledge bases, we propose a query generator model with contrastive instruction-tuning. As the query generator is separate from the frozen large language model, our framework is modular and thus easily adapted to various knowledge sources and models. Experiments show that our framework significantly enhances the factual correctness of large language models on knowledge-intensive tasks.

Knowledge Graph

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