mlsanityData to 14 Oct 2026

Do language models learn typicality judgments from text?

arXiv 2105.02987 ↗First posted 6 May 2021cs.CLcited by 32

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Abstract (arXiv)

Building on research arguing for the possibility of conceptual and categorical knowledge acquisition through statistics contained in language, we evaluate predictive language models (LMs) -- informed solely by textual input -- on a prevalent phenomenon in cognitive science: typicality. Inspired by experiments that involve language processing and show robust typicality effects in humans, we propose two tests for LMs. Our first test targets whether typicality modulates LM probabilities in assigning taxonomic category memberships to items. The second test investigates sensitivities to typicality in LMs' probabilities when extending new information about items to their categories. Both tests show modest -- but not completely absent -- correspondence between LMs and humans, suggesting that text-based exposure alone is insufficient to acquire typicality knowledge.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 9 of its 30 references that are in the map. First posted on arXiv 6 May 2021.

Builds on

9 of 30 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  5. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  6. 2019Language Models as Knowledge Bases?cited by 1,936
  7. 2019What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language modelscited by 320
  8. 2019BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Modelcited by 185
  9. 2020Exploring BERT's Sensitivity to Lexical Cues using Tests from Semantic Primingcited by 41

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 7; 2024: 8; 2025: 7; 2026: 2.

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  8. 2021On Semantic Cognition, Inductive Generalization, and Language Modelscited by 2
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  10. 2025Investigating Human-Aligned Large Language Model Uncertaintycited by 2
  11. 2025On Language Models' Sensitivity to Suspicious Coincidencescited by 2
  12. 2023Counteracts: Testing Stereotypical Representation in Pre-trained Language Modelscited by 1
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  18. 2025Basic Category Usage in Vision Language Modelscited by 0
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  20. 2026On the use of foundation models in cognitive sciencecited by 0