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Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases

Paper · 2021 · Boxi Cao, Hongyu Lin, Xianpei Han et al.

Summary

Do masked language models such as BERT actually store retrievable facts, or do they mainly exploit regularities in how they are evaluated? Cao et al. test this for three querying styles: bare relation prompts, prompts preceded by example subject–object pairs (case-based analogy), and prompts with added external context. Most experiments use BERT-large, with RoBERTa-large in the appendix. They run on the T-REx portion of LAMA and on WIKI-UNI, a new Wikidata-derived dataset with a much flatter answer distribution.

With prompts alone, the model's outputs barely change between the two datasets. They are largely fixed by the template even when the subject is removed. Strong LAMA scores therefore reflect how closely a prompt matches that dataset's answer skew. Example cases mainly steer the model toward objects of the right entity type, using types induced from the Wikidata taxonomy, but not toward the correct entity within that type. Contexts help mostly when they reveal the answer, either directly or in a recoverable form. The study casts doubt on treating MLMs as dependable knowledge bases and calls for de-biased probing benchmarks.

Key points

  • On LAMA, the five most frequent gold objects account for about 48% of instances, against under 8% on WIKI-UNI. Yet the model's top-5 predictions cover roughly 58% and 52% of instances, so its outputs look alike on both datasets.
  • Prompt-based precision falls from 30.36 on LAMA to 16.47 on WIKI-UNI. Three prompt families were tested: manual (Petroni et al.), mined (Jiang et al.), and auto-searched (Shin et al.).
  • Adding context lifts LAMA precision from 30.36 to 41.44. With the gold answer masked inside the context, precision still reaches 35.66.
  • When the masked answer can be reconstructed from the remaining context (60.23% of cases), the gain is +21.24 points. Otherwise the gain is only +6.99.
  • Code and data, including WIKI-UNI, are released in the LANKA repository (github.com/c-box/LANKA).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢TencentShenzhen · China66 works · 17,945 citations🏛️Chinese Academy of SciencesBeijing · China68 works · 22,753 citations🎓University of Chinese Academy of SciencesBeijing · China32 works · 7,615 citations🔬Institute of SoftwareBeijing · China10 works · 4,807 citations🔬State Key Laboratory of Computer ScienceBeijing · China4 works · 1,309 citations

Also on the byline: Mylan.

📚 Shelves 5 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Arts and Humanities · 9,296 works Language and Linguistics · 9,206 works Computer Vision and Pattern Recognition · 1,282 works

🧭 Topics 2 Language models & pretraining · 1,343 works Knowledge graphs & ontologies · 103 works

Cite

Cite as Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue and Jin Xu. 2021. Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.acl-long.146

@inproceedings{cao2021knowledgeable,
  title = {{Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases}},
  author = {Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue and Jin Xu},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2021},
  doi = {10.18653/v1/2021.acl-long.146},
  url = {https://doi.org/10.18653/v1/2021.acl-long.146}
}

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