On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT

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Measuring Causal Effects of Data Statistics on Language Model's 'Factual' Predictions (2022, background): “, 2020), singular and plural hypernymy probes (Ravichander et al., 2020) and common-sense constraints (Kassner et al.”
Enriching a Model's Notion of Belief using a Persistent Memory (2021, background): “, 2020), their answers to probing questions can be inconsistent (Elazar et al., 2021; Ravichander et al., 2020; Kassner and Schütze, 2020), making it hard to pin down what a model actually “believes””
A Review on Language Models as Knowledge Bases (2022, background): “To that end, several benchmarks have been proposed to measure consistency of LMs (Ravichander et al., 2020; Elazar et al., 2021), while other datasets have been adapted for that purpose (Levy et al., 2017; Wang et al., 2021c).”
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief (2021, background): “…PTLMs contain extensive world knowledge (Petroni et al., 2019; Roberts et al., 2020), they can be inconsistent in their answers to probing questions (Ettinger, 2020; Davison et al., 2019; Ravichander et al., 2020; Elazar et al., 2021; Subramanian et al., 2020), making their “world model” unclear.”