mlsanityData to 14 Oct 2026

Evaluating the Underlying Gender Bias in Contextualized Word Embeddings

arXiv 1904.08783 ↗First posted 18 Apr 2019cs.CL cs.LGcited by 94

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Abstract (arXiv)

Gender bias is highly impacting natural language processing applications. Word embeddings have clearly been proven both to keep and amplify gender biases that are present in current data sources. Recently, contextualized word embeddings have enhanced previous word embedding techniques by computing word vector representations dependent on the sentence they appear in. In this paper, we study the impact of this conceptual change in the word embedding computation in relation with gender bias. Our analysis includes different measures previously applied in the literature to standard word embeddings. Our findings suggest that contextualized word embeddings are less biased than standard ones even when the latter are debiased.

Key facts. Cited by 94 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Apr 2019.

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Citing papers per year: 2021: 9; 2022: 24; 2023: 20; 2024: 22; 2025: 15; 2026: 4.

94 papers.

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  11. 2022Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Modelscited by 68
  12. 2022Theories of "Gender" in NLP Bias Researchcited by 62
38 more
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  8. 2023Sociodemographic Bias in Language Models: A Survey and Forward Pathcited by 28
  9. 2022Interacting with next-phrase suggestions: How suggestion systems aid and influence the cognitive processes of writingcited by 27
  10. 2021Intersectional Bias in Causal Language Modelscited by 24
  11. 2022Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semanticscited by 24
  12. 2021Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue modelscited by 23
  13. 2023CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Modelscited by 23
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  15. 2021Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Modelscited by 20
  16. 2023Evaluating Biased Attitude Associations of Language Models in an Intersectional Contextcited by 20
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  18. 2024Parameter-Efficient Detoxification with Contrastive Decodingcited by 18
  19. 2024Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classificationcited by 18
  20. 2022The Birth of Bias: A case study on the evolution of gender bias in an English language modelcited by 16
  21. 2021Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Modelscited by 15
  22. 2023Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classificationscited by 15
  23. 2024Evaluating Gender Bias of LLMs in Making Morality Judgementscited by 15
  24. 2021Diachronic Analysis of German Parliamentary Proceedings: Ideological Shifts through the Lens of Political Biasescited by 13
  25. 2022A study on the distribution of social biases in self-supervised learning visual modelscited by 13
  26. 2022How Gender Debiasing Affects Internal Model Representations, and Why It Matterscited by 12
  27. 2023Toward Fairness in Text Generation via Mutual Information Minimization based on Importance Samplingcited by 12
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  29. 2022Experimental Standards for Deep Learning in Natural Language Processing Researchcited by 10
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  31. 2021Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsitycited by 9
  32. 2023An Invariant Learning Characterization of Controlled Text Generationcited by 9
  33. 2025Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategiescited by 9
  34. 2022Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Biascited by 8
  35. 2022Overcoming Language Disparity in Online Content Classification with Multimodal Learningcited by 8
  36. 2023Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddingscited by 8
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  38. 2024'Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMscited by 8

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