  Recently, there has been a trend to investigate the factual knowledge
captured by Pre-trained Language Models (PLMs). Many works show the PLMs'
ability to fill in the missing factual words in cloze-style prompts such as
"Dante was born in [MASK]." However, it is still a mystery how PLMs generate
the results correctly: relying on effective clues or shortcut patterns? We try
to answer this question by a causal-inspired analysis that quantitatively
measures and evaluates the word-level patterns that PLMs depend on to generate
the missing words. We check the words that have three typical associations with
the missing words: knowledge-dependent, positionally close, and highly
co-occurred. Our analysis shows: (1) PLMs generate the missing factual words
more by the positionally close and highly co-occurred words than the
knowledge-dependent words; (2) the dependence on the knowledge-dependent words
is more effective than the positionally close and highly co-occurred words.
Accordingly, we conclude that the PLMs capture the factual knowledge
ineffectively because of depending on the inadequate associations.
