  Despite the much discussed capabilities of today's language models, they are
still prone to silly and unexpected commonsense failures. We consider a
retrospective verification approach that reflects on the correctness of LM
outputs, and introduce Vera, a general-purpose model that estimates the
plausibility of declarative statements based on commonsense knowledge. Trained
on ~7M commonsense statements created from 19 QA datasets and two large-scale
knowledge bases, and with a combination of three training objectives, Vera is a
versatile model that effectively separates correct from incorrect statements
across diverse commonsense domains. When applied to solving commonsense
problems in the verification format, Vera substantially outperforms existing
models that can be repurposed for commonsense verification, and it further
exhibits generalization capabilities to unseen tasks and provides
well-calibrated outputs. We find that Vera excels at filtering LM-generated
commonsense knowledge and is useful in detecting erroneous commonsense
statements generated by models like ChatGPT in real-world settings.
