  Large language models can produce fluent dialogue but often hallucinate
factual inaccuracies. While retrieval-augmented models help alleviate this
issue, they still face a difficult challenge of both reasoning to provide
correct knowledge and generating conversation simultaneously. In this work, we
propose a modular model, Knowledge to Response (K2R), for incorporating
knowledge into conversational agents, which breaks down this problem into two
easier steps. K2R first generates a knowledge sequence, given a dialogue
context, as an intermediate step. After this "reasoning step", the model then
attends to its own generated knowledge sequence, as well as the dialogue
context, to produce a final response. In detailed experiments, we find that
such a model hallucinates less in knowledge-grounded dialogue tasks, and has
advantages in terms of interpretability and modularity. In particular, it can
be used to fuse QA and dialogue systems together to enable dialogue agents to
give knowledgeable answers, or QA models to give conversational responses in a
zero-shot setting.
