mlsanityData to 7 Oct 2026

Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge

arXiv 2112.08583 ↗First posted 16 Dec 2021cs.CLcited by 8

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Abstract (arXiv)

Transformer models pre-trained with a masked-language-modeling objective (e.g., BERT) encode commonsense knowledge as evidenced by behavioral probes; however, the extent to which this knowledge is acquired by systematic inference over the semantics of the pre-training corpora is an open question. To answer this question, we selectively inject verbalized knowledge into the minibatches of a BERT model during pre-training and evaluate how well the model generalizes to supported inferences. We find generalization does not improve over the course of pre-training, suggesting that commonsense knowledge is acquired from surface-level, co-occurrence patterns rather than induced, systematic reasoning.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 34 of its 49 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

34 of 49 references.

References · 34

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22 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 2; 2026: 1.

8 papers.