Pre-trained Language Models as Symbolic Reasoners over Knowledge?
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How can pre-trained language models (PLMs) learn factual knowledge from the training set? We investigate the two most important mechanisms: reasoning and memorization. Prior work has attempted to quantify the number of facts PLMs learn, but we present, using synthetic data, the first study that establishes a causal relation between facts present in training and facts learned by the PLM. For reasoning, we show that PLMs learn to apply some symbolic reasoning rules; but in particular, they struggle with two-hop reasoning. For memorization, we identify schema conformity (facts systematically supported by other facts) and frequency as key factors for its success.
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