mlsanityData to 7 Oct 2026

Mitigating Gender Bias in Natural Language Processing: Literature Review

arXiv 1906.08976 ↗First posted 21 Jun 2019cs.CLcited by 287

Not on the map. Why?

Abstract (arXiv)

As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.

Key facts. Cited by 287 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jun 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 54; 2022: 54; 2023: 61; 2024: 68; 2025: 36; 2026: 14.

287 papers.

Sort:

By area:

  1. 2024Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMscited by 719
  2. 2022Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Modelscited by 501
  3. 2021BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generationcited by 410
  4. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 394
  5. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 294
  6. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 257
  7. 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
  8. 2024From Persona to Personalization: A Survey on Role-Playing Language Agentscited by 206
  9. 2021Societal Biases in Language Generation: Progress and Challengescited by 201
  10. 2023Gender bias and stereotypes in Large Language Modelscited by 185
  11. 2021Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Docited by 178
  12. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
38 more
  1. 2023Debiasing Vision-Language Models via Biased Promptscited by 147
  2. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
  3. 2021Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologiescited by 128
  4. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  5. 2021Gender Bias in Machine Translationcited by 116
  6. 2021Measure and Improve Robustness in NLP Models: A Surveycited by 107
  7. 2023RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputscited by 104
  8. 2021Re-imagining Algorithmic Fairness in India and Beyondcited by 102
  9. 2023A Survey on Fairness in Large Language Modelscited by 91
  10. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
  11. 2021Pre-trained Language Models in Biomedical Domain: A Systematic Surveycited by 87
  12. 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metricscited by 83
  13. 2021On Measures of Biases and Harms in NLPcited by 81
  14. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
  15. 2024Bias in Large Language Models: Origin, Evaluation, and Mitigationcited by 77
  16. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 74
  17. 2021Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translationcited by 70
  18. 2023A Survey on Detection of LLMs-Generated Contentcited by 70
  19. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  20. 2022Handling Bias in Toxic Speech Detection: A Surveycited by 60
  21. 2021Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Modelscited by 57
  22. 2024Design Principles for Generative AI Applicationscited by 57
  23. 2022Re-contextualizing Fairness in NLP: The Case of Indiacited by 55
  24. 2021Changing the World by Changing the Datacited by 51
  25. 2024Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactionscited by 50
  26. 2022Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learningcited by 47
  27. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  28. 2021Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?cited by 46
  29. 2021They, Them, Theirs: Rewriting with Gender-Neutral Englishcited by 44
  30. 2021SelfExplain: A Self-Explaining Architecture for Neural Text Classifierscited by 44
  31. 2024Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attributioncited by 39
  32. 2021Your fairness may vary: Pretrained language model fairness in toxic text classificationcited by 38
  33. 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
  34. 2023Bias of AI-Generated Content: An Examination of News Produced by Large Language Modelscited by 38
  35. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  36. 2023Comparing Biases and the Impact of Multilingual Training across Multiple Languagescited by 36
  37. 2023Mass-Producing Failures of Multimodal Systems with Language Modelscited by 36
  38. 2021Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgettingcited by 34

50 most cited of 287 All 287 (JSON)