  Despite the striking advances in recent language generation performance,
model-generated responses have suffered from the chronic problem of
hallucinations that are either untrue or unfaithful to a given source.
Especially in the task of knowledge grounded conversation, the models are
required to generate informative responses, but hallucinated utterances lead to
miscommunication. In particular, entity-level hallucination that causes
critical misinformation and undesirable conversation is one of the major
concerns. To address this issue, we propose a post-hoc refinement method called
REM. It aims to enhance the quality and faithfulness of hallucinated utterances
by refining them based on the source knowledge. If the generated utterance has
a low source-faithfulness score with the given knowledge, REM mines the key
entities in the knowledge and implicitly uses them for refining the utterances.
We verify that our method reduces entity hallucination in the utterance. Also,
we show the adaptability and efficacy of REM with extensive experiments and
generative results. Our code is available at https://github.com/YOONNAJANG/REM.
