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On Measuring and Mitigating Biased Inferences of Word Embeddings

arXiv 1908.09369 ↗First posted 25 Aug 2019cs.CL cs.LGcited by 112

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Abstract (arXiv)

Word embeddings carry stereotypical connotations from the text they are trained on, which can lead to invalid inferences in downstream models that rely on them. We use this observation to design a mechanism for measuring stereotypes using the task of natural language inference. We demonstrate a reduction in invalid inferences via bias mitigation strategies on static word embeddings (GloVe). Further, we show that for gender bias, these techniques extend to contextualized embeddings when applied selectively only to the static components of contextualized embeddings (ELMo, BERT).

Key facts. Cited by 112 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Aug 2019.

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Citing papers per year: 2021: 17; 2022: 21; 2023: 24; 2024: 28; 2025: 16; 2026: 6.

112 papers.

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  1. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  2. 2022Galactica: A Large Language Model for Sciencecited by 736
  3. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
  4. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  5. 2021Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLPcited by 336
  6. 2023Large Language Model Alignment: A Surveycited by 241
  7. 2023Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMscited by 191
  8. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  9. 2021Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologiescited by 128
  10. 2021Debiasing Pre-trained Contextualised Embeddingscited by 107
  11. 2021Sustainable Modular Debiasing of Language Modelscited by 98
  12. 2023A Survey on Fairness in Large Language Modelscited by 91
38 more
  1. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
  2. 2021On Measures of Biases and Harms in NLPcited by 81
  3. 2024Bias in Large Language Models: Origin, Evaluation, and Mitigationcited by 77
  4. 2023SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Modelscited by 66
  5. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  6. 2021Unmasking the Mask -- Evaluating Social Biases in Masked Language Modelscited by 60
  7. 2022Gender Bias in Masked Language Models for Multiple Languagescited by 60
  8. 2022AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Modelcited by 57
  9. 2024Fairness in Large Language Models: A Taxonomic Surveycited by 57
  10. 2022Re-contextualizing Fairness in NLP: The Case of Indiacited by 55
  11. 2023Nationality Bias in Text Generationcited by 50
  12. 2022Debiasing isn't enough! -- On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Taskscited by 48
  13. 2022BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generationcited by 48
  14. 2022Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gendercited by 47
  15. 2021Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?cited by 46
  16. 2022Quantifying Social Biases Using Templates is Unreliablecited by 38
  17. 2022The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarkscited by 31
  18. 2022MABEL: Attenuating Gender Bias using Textual Entailment Datacited by 31
  19. 2022Choose Your Lenses: Flaws in Gender Bias Evaluationcited by 29
  20. 2024LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Educationcited by 28
  21. 2023Sociodemographic Bias in Language Models: A Survey and Forward Pathcited by 27
  22. 2024Exploring Safety-Utility Trade-Offs in Personalized Language Modelscited by 26
  23. 2023Building Socio-culturally Inclusive Stereotype Resources with Community Engagementcited by 25
  24. 2024Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approachcited by 25
  25. 2024SeeGULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypescited by 24
  26. 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Surveycited by 23
  27. 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
  28. 2023Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biasescited by 21
  29. 2021Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Modelscited by 20
  30. 2022Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddingscited by 18
  31. 2022Fair and Argumentative Language Modeling for Computational Argumentationcited by 18
  32. 2023Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All Labelscited by 16
  33. 2024Large Language Model Bias Mitigation from the Perspective of Knowledge Editingcited by 16
  34. 2024Large Language Models Still Exhibit Bias in Long Textcited by 16
  35. 2023Co2PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuningcited by 15
  36. 2024Ask LLMs Directly, "What shapes your bias?": Measuring Social Bias in Large Language Modelscited by 14
  37. 2023CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Biascited by 13
  38. 2024Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extractioncited by 13

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