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A Survey on Stereotype Detection in Natural Language Processing

arXiv 2505.17642 ↗First posted 23 May 2025cs.CL cs.CYcited by 0

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Abstract (arXiv)

Stereotypes influence social perceptions and can escalate into discrimination and violence. While NLP research has extensively addressed gender bias and hate speech, stereotype detection remains an emerging field with significant societal implications. In this work is presented a survey of existing research, analyzing definitions from psychology, sociology, and philosophy. A semi-automatic literature review was performed by using Semantic Scholar. We retrieved and filtered over 6,000 papers (in the year range 2000-2025), identifying key trends, methodologies, challenges and future directions. The findings emphasize stereotype detection as a potential early-monitoring tool to prevent bias escalation and the rise of hate speech. Conclusions highlight the need for a broader, multilingual, and intersectional approach in NLP studies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 90 references that are in the map. First posted on arXiv 23 May 2025.

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31 of 90 references.

References · 31

  1. 2015A large annotated corpus for learning natural language inferencecited by 1,847
  2. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 924
  3. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 710
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  5. 2023Toxicity in ChatGPT: Analyzing Persona-assigned Language Modelscited by 360
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  9. 2022On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoningcited by 194
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  11. 2019Assessing Social and Intersectional Biases in Contextualized Word Representationscited by 135
  12. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
19 more
  1. 2023SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Modelscited by 66
  2. 2021Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translationcited by 62
  3. 2022Handling Bias in Toxic Speech Detection: A Surveycited by 61
  4. 2022Re-contextualizing Fairness in NLP: The Case of Indiacited by 55
  5. 2022Handling and Presenting Harmful Text in NLP Researchcited by 43
  6. 2022Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Modelscited by 35
  7. 2021Contrastive Learning for Fair Representationscited by 34
  8. 2023Queer In AI: A Case Study in Community-Led Participatory AIcited by 34
  9. 2023A Trip Towards Fairness: Bias and De-Biasing in Large Language Modelscited by 32
  10. 2020A Framework for the Computational Linguistic Analysis of Dehumanizationcited by 31
  11. 2021Evaluating Debiasing Techniques for Intersectional Biasescited by 30
  12. 2023On Hate Scaling Laws For Data-Swampscited by 28
  13. 2022A Robust Bias Mitigation Procedure Based on the Stereotype Content Modelcited by 24
  14. 2021Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Modelcited by 23
  15. 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
  16. 2020Case Study: Deontological Ethics in NLPcited by 12
  17. 2021Automatic Sexism Detection with Multilingual Transformer Modelscited by 9
  18. 2022Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detectioncited by 7
  19. 2024Quantifying Stereotypes in Languagecited by 4

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