Existing knowledge-grounded conversation systems generate responses typically
in a retrieve-then-generate manner. They require a large knowledge base and a
strong knowledge retrieval component, which is time- and resource-consuming. In
this paper, we address the challenge by leveraging the inherent knowledge
encoded in the pre-trained language models (PLMs). We propose Knowledgeable
Prefix Tuning (KnowPrefix-Tuning), a two-stage tuning framework, bypassing the
retrieval process in a knowledge-grounded conversation system by injecting
prior knowledge into the lightweight knowledge prefix. The knowledge prefix is
a sequence of continuous knowledge-specific vectors that can be learned during
training. In addition, we propose a novel interactive re-parameterization
mechanism that allows the prefix to interact fully with the PLM during the
optimization of response generation. Experimental results demonstrate that
KnowPrefix-Tuning outperforms fine-tuning and other lightweight tuning
approaches, and performs comparably with strong retrieval-based baselines while
being $3\times$ faster during inference.

为了简化知识导引对话系统的检索过程，本文提出了一种利用预训练语言模型中的固有知识编码的轻量级知识前缀注入方法，称为 Knowledgeable Prefix Tuning，并提出一种新颖的交互式重新参数化机制，使前缀完全与语言模型交互，并在实验中得到了成功的应用。