Negated LAMA: Birds cannot fly

By Nora Kassner, Hinrich Schütze. 2019

23 citations recorded by the source corpus.

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Citations in the corpus

How Context Affects Language Models' Factual Predictions (2020, background): “Jiang et al. [2019] later extends LAMA by automatically discovering better prompts, Kassner and Sch¨utze [2019] add negated statements, Poerner et al. [2019] ﬁlter out easy-to-guess queries, and Richardson and Sabharwal [2019], Talmor et al. [2019], Bisk et al. [2019] develop further probes for…”
oLMpics-On What Language Model Pre-training Captures (2019, background): “Several recent works have shown that indeed LMs do not take into account the presence of negation in sentences (Ettinger, 2019; Nie et al., 2019; Kassner and Schütze, 2019).”
Unsupervised Commonsense Question Answering with Self-Talk (2020, background): “Specifically, Kassner and Schütze (2019) showed that negated facts are also considered likely by the LM, while Logan et al.”
Pre-trained models for natural language processing: A survey (2020, neutral): “Despite the surprising findings of LAMA, it has also been questioned by subsequent work [141, 82].”
What Do Models Learn from Question Answering Datasets? (2020, methodology): “In previous work, Kassner and Sch¨utze (2019) For each dataset, we negated every question in the test set by mapping common verbs (i.e. is , did , has ) to their contracted negative form (i.e. isn’t , didn’t , hasn’t ) or by inserting never before the main verb, as identiﬁed by spaCy.”
What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge (2019, background): “Related to our efforts, there have been recent studies into the types of relational knowledge contained in large-scale knowledge models (Petroni et al., 2019; Kassner and Schütze, 2019; Jiang et al., 2019), which, similar to our work, probe models using structured knowledge sources.”
Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems (2020, background): “Moreover, several studies tried to extract (Petroni et al., 2019; Kassner and Sch¨utze, 2019; Petroni et al., 2020) or use (Roberts et al., 2020) large pre-trained models, e.g. BERT (Devlin et al., 2019), as knowledge bases.”