KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for
  Knowledge-Grounded Dialogue Generation
Jiaqi Bai Zhao Yan Jian Yang Xinnian Liang Hongcheng Guo Zhoujun Li
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.