Reason first, then respond: Modular Generation for Knowledge-infused Dialogue

Understand

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.