mlsanityData to 14 Oct 2026

Systematic Offensive Stereotyping (SOS) Bias in Language Models

arXiv 2308.10684 ↗First posted 21 Aug 2023cs.CLcited by 1

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Abstract (arXiv)

In this paper, we propose a new metric to measure the SOS bias in language models (LMs). Then, we validate the SOS bias and investigate the effectiveness of removing it. Finally, we investigate the impact of the SOS bias in LMs on their performance and fairness on hate speech detection. Our results suggest that all the inspected LMs are SOS biased. And that the SOS bias is reflective of the online hate experienced by marginalized identities. The results indicate that using debias methods from the literature worsens the SOS bias in LMs for some sensitive attributes and improves it for others. Finally, Our results suggest that the SOS bias in the inspected LMs has an impact on their fairness of hate speech detection. However, there is no strong evidence that the SOS bias has an impact on the performance of hate speech detection.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 36 references that are in the map. First posted on arXiv 21 Aug 2023.

Builds on

14 of 36 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
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  5. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  6. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 710
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  8. 2019On Measuring Social Biases in Sentence Encoderscited by 398
  9. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  10. 2016Ex Machina: Personal Attacks Seen at Scalecited by 210
  11. 2020Towards Debiasing Sentence Representationscited by 171
  12. 2020Intrinsic Bias Metrics Do Not Correlate with Application Biascited by 148
2 more
  1. 2019Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overviewcited by 137
  2. 2021An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Modelscited by 97

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detectioncited by 2