mlsanityData to 7 Oct 2026

Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models

arXiv 2206.11684 ↗First posted 23 Jun 2022cs.CLcited by 35

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Abstract (arXiv)

NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale. We adapt the Agency-Belief-Communion (ABC) stereotype model of Koch et al. (2016) from social psychology as a framework for the systematic study and discovery of stereotypic group-trait associations in language models (LMs). We introduce the sensitivity test (SeT) for measuring stereotypical associations from language models. To evaluate SeT and other measures using the ABC model, we collect group-trait judgments from U.S.-based subjects to compare with English LM stereotypes. Finally, we extend this framework to measure LM stereotyping of intersectional identities.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 13 of its 54 references that are in the map. First posted on arXiv 23 Jun 2022.

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13 of 54 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2018Datasheets for Datasetscited by 1,371
  4. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  5. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
  6. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  7. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 703
  8. 2019On Measuring Social Biases in Sentence Encoderscited by 397
  9. 2019Social Bias Frames: Reasoning about Social and Power Implications of Languagecited by 377
  10. 2019Measuring Bias in Contextualized Word Representationscited by 275
  11. 2020Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biasescited by 167
  12. 2022On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representationscited by 75
1 more
  1. 2020On the Social and Technical Challenges of Web Search Autosuggestion Moderationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 9; 2024: 11; 2025: 11; 2026: 1.

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  1. 2023Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Modelscited by 204
  2. 2023Having Beer after Prayer? Measuring Cultural Bias in Large Language Modelscited by 153
  3. 2023Aligning Language Models with Preferences through f-divergence Minimizationcited by 118
  4. 2023CoMPosT: Characterizing and Evaluating Caricature in LLM Simulationscited by 94
  5. 2023WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Modelscited by 42
  6. 2024Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attributioncited by 39
  7. 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
  8. 2025Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMscited by 26
  9. 2023A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?cited by 25
  10. 2022A Robust Bias Mitigation Procedure Based on the Stereotype Content Modelcited by 24
  11. 2022SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Modelscited by 22
  12. 2023Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreementcited by 22
23 more
  1. 2025BiasEdit: Debiasing Stereotyped Language Models via Model Editingcited by 18
  2. 2024GPT is Not an Annotator: The Necessity of Human Annotation in Fairness Benchmark Constructioncited by 15
  3. 2023Multilingual large language models leak human stereotypes across language boundariescited by 12
  4. 2025Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategiescited by 8
  5. 2025Language Models Predict Empathy Gaps Between Social In-groups and Out-groupscited by 6
  6. 2025Who's Asking? Investigating Bias Through the Lens of Disability Framed Queries in LLMscited by 6
  7. 2022Undesirable Biases in NLP: Addressing Challenges of Measurementcited by 4
  8. 2024Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Wayscited by 4
  9. 2024Laissez-Faire Harms: Algorithmic Biases in Generative Language Modelscited by 4
  10. 2025LLMs Reproduce Stereotypes of Sexual and Gender Minoritiescited by 4
  11. 2023disco: a toolkit for Distributional Control of Generative Modelscited by 3
  12. 2024With a Grain of SALT: Are LLMs Fair Across Social Dimensions?cited by 3
  13. 2025EtiCor++: Towards Understanding Etiquettical Bias in LLMscited by 3
  14. 2024A Taxonomy of Stereotype Content in Large Language Modelscited by 2
  15. 2024When Stereotypes GTG: The Impact of Predictive Text Suggestions on Gender Bias in Human-AI Co-Writingcited by 2
  16. 2025Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypescited by 2
  17. 2025Artificial Impressions: Evaluating Large Language Model Behavior Through the Lens of Trait Impressionscited by 2
  18. 2024Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Modelscited by 1
  19. 2024Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Modelscited by 1
  20. 2024Visual Stereotypes of Autism Spectrum in Janus-Pro-7B, DALL-E, Stable Diffusion, SDXL, FLUX, and Midjourneycited by 0
  21. 2024On the Influence of Gender and Race in Romantic Relationship Prediction from Large Language Modelscited by 0
  22. 2025A Survey on Stereotype Detection in Natural Language Processingcited by 0
  23. 2026On Defining Erasure Harms for NLPcited by 0