Open-domain dialogue system usually requires different sources of knowledge
to generate more informative and evidential responses. However, existing
knowledge-grounded dialogue systems either focus on a single knowledge source
or overlook the dependency between multiple sources of knowledge, which may
result in generating inconsistent or even paradoxical responses. To incorporate
multiple knowledge sources and dependencies between them, we propose SAFARI, a
novel framework that leverages the exceptional capabilities of large language
models (LLMs) in planning, understanding, and incorporating under both
supervised and unsupervised settings. Specifically, SAFARI decouples the
knowledge grounding into multiple sources and response generation, which allows
easy extension to various knowledge sources including the possibility of not
using any sources. To study the problem, we construct a personalized
knowledge-grounded dialogue dataset \textit{\textbf{K}nowledge \textbf{B}ehind
\textbf{P}ersona}~(\textbf{KBP}), which is the first to consider the dependency
between persona and implicit knowledge. Experimental results on the KBP dataset
demonstrate that the SAFARI framework can effectively produce
persona-consistent and knowledge-enhanced responses.

用大型语言模型（LLM）计划、理解和整合多个知识来源，SAFARI 框架提供了一个解决在开放领域对话系统中多个知识源之间依赖关系问题的新方法，该框架在 KBPD 数据集上的实验结果表明其能够有效地生成与人物形象一致且知识增强的回复。