  Recent advances in large-scale pre-training provide large models with the
potential to learn knowledge from the raw text. It is thus natural to ask
whether it is possible to leverage these large models as knowledge bases for
downstream tasks. In this work, we answer the aforementioned question in
unsupervised knowledge-grounded conversation. We explore various methods that
best elicit knowledge from large models. Our human study indicates that, though
hallucinations exist, large models post the unique advantage of being able to
output common sense and summarize facts that cannot be directly retrieved from
the search engine. To better exploit such generated knowledge in dialogue
generation, we treat the generated knowledge as a noisy knowledge source and
propose the posterior-based reweighing as well as the noisy training strategy.
Empirical results on two benchmarks show advantages over the state-of-the-art
methods.
