mlsanityData to 14 Oct 2026

Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models

arXiv 2104.07505 ↗First posted 15 Apr 2021cs.CL stat.MLcited by 15

Not on the map. Why?

Abstract (arXiv)

Recent research has demonstrated that large pre-trained language models reflect societal biases expressed in natural language. The present paper introduces a simple method for probing language models to conduct a multilingual study of gender bias towards politicians. We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. To this end, we curate a dataset of 250k politicians worldwide, including their names and gender. Our study is conducted in seven languages across six different language modeling architectures. The results demonstrate that pre-trained language models' stance towards politicians varies strongly across analyzed languages. We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians. Finally, and contrary to previous findings, our study suggests that larger language models do not tend to be significantly more gender-biased than smaller ones.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 33 of its 96 references that are in the map. First posted on arXiv 15 Apr 2021.

Builds on

33 of 96 references.

References · 33

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  5. 2013Auto-Encoding Variational Bayescited by 4,053
  6. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  7. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  8. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  9. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
  10. 2019Cross-lingual Language Model Pretrainingcited by 1,090
  11. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  12. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 924
21 more
  1. 2020A Primer in BERTology: What we know about how BERT workscited by 815
  2. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 710
  3. 2019What do you learn from context? Probing for sentence structure in contextualized word representationscited by 438
  4. 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
  5. 2019On Measuring Social Biases in Sentence Encoderscited by 398
  6. 2019Linguistic Knowledge and Transferability of Contextual Representationscited by 383
  7. 2020BERTweet: A pre-trained language model for English Tweetscited by 346
  8. 2020Measuring and Reducing Gendered Correlations in Pre-trained Modelscited by 224
  9. 2020Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Biascited by 191
  10. 2019BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Modelcited by 185
  11. 2019Evaluating the Underlying Gender Bias in Contextualized Word Embeddingscited by 94
  12. 2020Multi-Dimensional Gender Bias Classificationcited by 80
  13. 2019Perturbation Sensitivity Analysis to Detect Unintended Model Biasescited by 74
  14. 2019In Plain Sight: Media Bias Through the Lens of Factual Reportingcited by 68
  15. 2016Stance and Sentiment in Tweetscited by 62
  16. 2020AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languagescited by 44
  17. 2020"You are grounded!": Latent Name Artifacts in Pre-trained Language Modelscited by 39
  18. 2015It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopediacited by 30
  19. 2019Unsupervised Discovery of Gendered Language through Latent-Variable Modelingcited by 19
  20. 2019Combining Sentiment Lexica with a Multi-View Variational Autoencodercited by 2
  21. 2019UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languagescited by 2

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 5; 2024: 5; 2025: 1.

15 papers.

Sort:

By area:

  1. 2022Probing Pre-Trained Language Models for Cross-Cultural Differences in Valuescited by 149
  2. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
  3. 2023Large Language Models as Superpositions of Cultural Perspectivescited by 49
  4. 2024Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personascited by 37
  5. 2023Social Bias Probing: Fairness Benchmarking for Language Modelscited by 32
  6. 2023Measuring Gender Bias in West Slavic Language Modelscited by 10
  7. 2024The Echoes of Multilinguality: Tracing Cultural Value Shifts during LM Fine-tuningcited by 10
  8. 2022Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Biascited by 8
  9. 2024Polarization of Autonomous Generative AI Agents Under Echo Chamberscited by 7
  10. 2021Quantifying Gender Biases Towards Politicians on Redditcited by 5
  11. 2024Leveraging Large Language Models to Measure Gender Representation Bias in Gendered Language Corporacited by 5
  12. 2024Revealing Fine-Grained Values and Opinions in Large Language Modelscited by 5
3 more
  1. 2023Detecting Natural Language Biases with Prompt-based Learningcited by 1
  2. 2023The Ethics of Automating Legal Actorscited by 0
  3. 2025Local Differences, Global Lessons: Insights from Organisation Policies for International Legislationcited by 0