Are Pretrained Language Models Symbolic Reasoners Over Knowledge?
- URL: http://arxiv.org/abs/2006.10413v2
- Date: Sat, 10 Oct 2020 10:09:46 GMT
- Title: Are Pretrained Language Models Symbolic Reasoners Over Knowledge?
- Authors: Nora Kassner, Benno Krojer, Hinrich Sch\"utze
- Abstract summary: We investigate the two most important mechanisms: reasoning and memorization.
For reasoning, we show that PLMs seem to learn to apply some symbolic reasoning rules correctly but struggle with others, including two-hop reasoning.
For memorization, we identify schema conformity (facts systematically supported by other facts) and frequency as key factors for its success.
- Score: 5.480912891689259
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: How can pretrained 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 investigates
the causal relation between facts present in training and facts learned by the
PLM. For reasoning, we show that PLMs seem to learn to apply some symbolic
reasoning rules correctly but struggle with others, including two-hop
reasoning. Further analysis suggests that even the application of learned
reasoning rules is flawed. For memorization, we identify schema conformity
(facts systematically supported by other facts) and frequency as key factors
for its success.
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