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Recent work has investigated the interesting question using pre-trainedlanguage models (PLMs) as knowledge bases for answering open questions.However, existing work is limited in using small benchmarks with hightest-train overlaps. We construct a new dataset of closed-book QA using SQuAD,and investigate the performance of BART. Experiments show that it ischallenging for BART to remember training facts in high precision, and alsochallenging to answer closed-book questions even if relevant knowledge isretained. Some promising directions are found, including decoupling theknowledge memorizing process and the QA finetune process, forcing the model torecall relevant knowledge when question answering.

Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?