mlsanityData to 14 Oct 2026

An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models

arXiv 2110.08527 ↗First posted 16 Oct 2021cs.CL cs.LGcited by 97

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Abstract (arXiv)

Recent work has shown pre-trained language models capture social biases from the large amounts of text they are trained on. This has attracted attention to developing techniques that mitigate such biases. In this work, we perform an empirical survey of five recently proposed bias mitigation techniques: Counterfactual Data Augmentation (CDA), Dropout, Iterative Nullspace Projection, Self-Debias, and SentenceDebias. We quantify the effectiveness of each technique using three intrinsic bias benchmarks while also measuring the impact of these techniques on a model's language modeling ability, as well as its performance on downstream NLU tasks. We experimentally find that: (1) Self-Debias is the strongest debiasing technique, obtaining improved scores on all bias benchmarks; (2) Current debiasing techniques perform less consistently when mitigating non-gender biases; And (3) improvements on bias benchmarks such as StereoSet and CrowS-Pairs by using debiasing strategies are often accompanied by a decrease in language modeling ability, making it difficult to determine whether the bias mitigation was effective.

Key facts. Cited by 97 papers in the map (data through 14 Oct 2026). Builds on 32 of its 48 references that are in the map. First posted on arXiv 16 Oct 2021.

Builds on

32 of 48 references.

References · 32

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  4. 2016Pointer Sentinel Mixture Modelscited by 3,096
  5. 2018Deep contextualized word representationscited by 2,290
  6. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  7. 2019How Can We Know What Language Models Know?cited by 959
  8. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  9. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 924
  10. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  11. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 710
  12. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 659
20 more
  1. 2021Datasets: A Community Library for Natural Language Processingcited by 530
  2. 2020Null It Out: Guarding Protected Attributes by Iterative Nullspace Projectioncited by 455
  3. 2019On Measuring Social Biases in Sentence Encoderscited by 398
  4. 2021Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLPcited by 337
  5. 2020Calibration of Pre-trained Transformerscited by 315
  6. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 296
  7. 2019Measuring Bias in Contextualized Word Representationscited by 276
  8. 2020Measuring and Reducing Gendered Correlations in Pre-trained Modelscited by 224
  9. 2019Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphologycited by 200
  10. 2019BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Modelcited by 185
  11. 2020Towards Debiasing Sentence Representationscited by 171
  12. 2019Queens are Powerful too: Mitigating Gender Bias in Dialogue Generationcited by 152
  13. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  14. 2019Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddingscited by 147
  15. 2021Debiasing Pre-trained Contextualised Embeddingscited by 107
  16. 2021Sustainable Modular Debiasing of Language Modelscited by 100
  17. 2020Multi-Dimensional Gender Bias Classificationcited by 80
  18. 2021FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoderscited by 75
  19. 2019Examining Gender Bias in Languages with Grammatical Gendercited by 40
  20. 2021How Reliable are Model Diagnostics?cited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 22; 2023: 37; 2024: 22; 2025: 6; 2026: 10.

97 papers.

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  1. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 387
  2. 2022On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoningcited by 194
  3. 2023A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicitycited by 182
  4. 2023The Capacity for Moral Self-Correction in Large Language Modelscited by 173
  5. 2023Large-scale Multi-Modal Pre-trained Models: A Comprehensive Surveycited by 153
  6. 2023Language Model Behavior: A Comprehensive Surveycited by 92
  7. 2024A Survey on Multilingual Large Language Models: Corpora, Alignment, and Biascited by 84
  8. 2024Fairness in Large Language Models: A Taxonomic Surveycited by 58
  9. 2022Perturbation Augmentation for Fairer NLPcited by 52
  10. 2023Using In-Context Learning to Improve Dialogue Safetycited by 42
  11. 2023T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generationcited by 39
  12. 2022MABEL: Attenuating Gender Bias using Textual Entailment Datacited by 31
38 more
  1. 2023On Evaluating and Mitigating Gender Biases in Multilingual Settingscited by 31
  2. 2023Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generationcited by 31
  3. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  4. 2024History, Development, and Principles of Large Language Models-An Introductory Surveycited by 30
  5. 2022On the Impossible Safety of Large AI Modelscited by 28
  6. 2023Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventionscited by 28
  7. 2023Survey of Social Bias in Vision-Language Modelscited by 26
  8. 2024Large Language Models and Causal Inference in Collaboration: A Surveycited by 26
  9. 2024Large Language Models as Recommender Systems: A Study of Popularity Biascited by 26
  10. 2024Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Artcited by 26
  11. 2023Uncovering and Quantifying Social Biases in Code Generationcited by 25
  12. 2024"They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversationscited by 25
  13. 2024Causality for Large Language Modelscited by 25
  14. 2022A Robust Bias Mitigation Procedure Based on the Stereotype Content Modelcited by 24
  15. 2024Can We Talk Models Into Seeing the World Differently?cited by 23
  16. 2022SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Modelscited by 22
  17. 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
  18. 2022ADEPT: A DEbiasing PrompT Frameworkcited by 21
  19. 2022Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairnesscited by 21
  20. 2023Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biasescited by 21
  21. 2022FairCLIP: Social Bias Elimination based on Attribute Prototype Learning and Representation Neutralizationcited by 20
  22. 2024A Causal Explainable Guardrails for Large Language Modelscited by 18
  23. 2023Fairness in Language Models Beyond English: Gaps and Challengescited by 16
  24. 2023An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Modelscited by 16
  25. 2023Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniquescited by 14
  26. 2023Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Headscited by 14
  27. 2023Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Modelscited by 12
  28. 2024OxonFair: A Flexible Toolkit for Algorithmic Fairnesscited by 12
  29. 2024Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approachcited by 12
  30. 2022Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animalscited by 11
  31. 2023Should We Attend More or Less? Modulating Attention for Fairnesscited by 11
  32. 2024Endowing Pre-trained Graph Models with Provable Fairnesscited by 10
  33. 2026Position: LLMs Must Use Functor-Based and RAG-Driven Bias Mitigation for Fairnesscited by 10
  34. 2024Can Machines Think Like Humans? A Behavioral Evaluation of LLM Agents in Dictator Gamescited by 9
  35. 2026CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMscited by 9
  36. 2023In-Depth Look at Word Filling Societal Bias Measurescited by 8
  37. 2023Mitigating Bias for Question Answering Models by Tracking Bias Influencecited by 8
  38. 2022What Changed? Investigating Debiasing Methods using Causal Mediation Analysiscited by 7

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