Large Language Models as Source Planner for Personalized
  Knowledge-grounded Dialogue
Hongru Wang Minda Hu Yang Deng Rui Wang Fei Mi Weichao Wang Yasheng Wang Wai-Chung Kwan Irwin King Kam-Fai Wong
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.