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。

大型语言模型作为个性化知识驱动对话的源计划器

Large Language Models as Source Planner for Personalized  Knowledge-grounded Dialogue

Knowledge-grounded conversation (KGC) shows great potential in building an
engaging and knowledgeable chatbot, and knowledge selection is a key ingredient
in it. However, previous methods for knowledge selection only concentrate on
the relevance between knowledge and dialogue context, ignoring the fact that
age, hobby, education and life experience of an interlocutor have a major
effect on his or her personal preference over external knowledge. Without
taking the personalization issue into account, it is difficult to select the
proper knowledge and generate persona-consistent responses. In this work, we
introduce personal memory into knowledge selection in KGC to address the
personalization issue. We propose a variational method to model the underlying
relationship between one's personal memory and his or her selection of
knowledge, and devise a learning scheme in which the forward mapping from
personal memory to knowledge and its inverse mapping is included in a closed
loop so that they could teach each other. Experiment results show that our
method outperforms existing KGC methods significantly on both automatic
evaluation and human evaluation.

本文介绍了将个人记忆引入知识选择的方法，以解决个性化的问题，采用变分方法建模个人记忆与知识选择之间的关系，并设计了一种学习方式，其中包括了从个人记忆到知识的前向映射和其逆向映射。实验证明，我们的方法在自动评估和人类评估方面都显著优于现有的知识驱动对话方法。