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How can pretrained language models (PLMs) learn factual knowledge from thetraining set? We investigate the two most important mechanisms: reasoning andmemorization. Prior work has attempted to quantify the number of facts PLMslearn, but we present, using synthetic data, the first study that investigatesthe causal relation between facts present in training and facts learned by thePLM. For reasoning, we show that PLMs seem to learn to apply some symbolicreasoning rules correctly but struggle with others, including two-hopreasoning. Further analysis suggests that even the application of learnedreasoning rules is flawed. For memorization, we identify schema conformity(facts systematically supported by other facts) and frequency as key factorsfor its success.

Are Pretrained Language Models Symbolic Reasoners Over Knowledge?