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.

翻译：我们提出知识链（Chain of Knowledge, CoK）框架，该框架通过增强大型语言模型与结构化知识库的结合，提升事实准确性并减少幻觉现象。与仅检索非结构化文本的现有研究不同，CoK利用支持复杂查询且提供更直接事实陈述的结构化知识库。为协助大型语言模型有效查询知识库，我们提出一种基于对比指令微调的查询生成器模型。由于查询生成器与冻结的大型语言模型相独立，该框架具有模块化特性，可灵活适配多种知识源与模型。实验表明，本框架在知识密集型任务中显著提升了大型语言模型的事实准确性。