  A common thread of retrieval-augmented methods in the existing literature
focuses on retrieving encyclopedic knowledge, such as Wikipedia, which
facilitates well-defined entity and relation spaces that can be modeled.
However, applying such methods to commonsense reasoning tasks faces two unique
challenges, i.e., the lack of a general large-scale corpus for retrieval and a
corresponding effective commonsense retriever. In this paper, we systematically
investigate how to leverage commonsense knowledge retrieval to improve
commonsense reasoning tasks. We proposed a unified framework of
retrieval-augmented commonsense reasoning (called RACo), including a newly
constructed commonsense corpus with over 20 million documents and novel
strategies for training a commonsense retriever. We conducted experiments on
four different commonsense reasoning tasks. Extensive evaluation results showed
that our proposed RACo can significantly outperform other knowledge-enhanced
method counterparts, achieving new SoTA performance on the CommonGen and CREAK
leaderboards.
