  Human-like chatbots necessitate the use of commonsense reasoning in order to
effectively comprehend and respond to implicit information present within
conversations. Achieving such coherence and informativeness in responses,
however, is a non-trivial task. Even for large language models (LLMs), the task
of identifying and aggregating key evidence within a single hop presents a
substantial challenge. This complexity arises because such evidence is
scattered across multiple turns in a conversation, thus necessitating
integration over multiple hops. Hence, our focus is to facilitate such
multi-hop reasoning over a dialogue context, namely dialogue chain-of-thought
(CoT) reasoning. To this end, we propose a knowledge distillation framework
that leverages LLMs as unreliable teachers and selectively distills consistent
and helpful rationales via alignment filters. We further present DOCTOR, a
DialOgue Chain-of-ThOught Reasoner that provides reliable CoT rationales for
response generation. We conduct extensive experiments to show that enhancing
dialogue agents with high-quality rationales from DOCTOR significantly improves
the quality of their responses.
