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We study knowledge-grounded dialogue generation with pre-trained languagemodels. Instead of pursuing new state-of-the-art on benchmarks, we try tounderstand if the knowledge stored in parameters of the pre-trained models isalready enough to ground open domain dialogues, and thus allows us to get ridof the dependency on external knowledge sources in generation. Throughextensive experiments on benchmarks, we find that by fine-tuning with a fewdialogues containing knowledge, the pre-trained language models can outperformthe state-of-the-art model that requires external knowledge in automaticevaluation and human judgment, suggesting a positive answer to the question weraised.

Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues?