@inproceedings{li-etal-2022-pre,
    title = "How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis",
    author = "Li, Shaobo  and
      Li, Xiaoguang  and
      Shang, Lifeng  and
      Dong, Zhenhua  and
      Sun, Chengjie  and
      Liu, Bingquan  and
      Ji, Zhenzhou  and
      Jiang, Xin  and
      Liu, Qun",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-acl.136",
    doi = "10.18653/v1/2022.findings-acl.136",
    pages = "1720--1732",
    abstract = "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.",
}