mlsanityData to 7 Oct 2026

MISGENDERED: Limits of Large Language Models in Understanding Pronouns

arXiv 2306.03950 ↗First posted 6 Jun 2023cs.CLcited by 43

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Abstract (arXiv)

Content Warning: This paper contains examples of misgendering and erasure that could be offensive and potentially triggering. Gender bias in language technologies has been widely studied, but research has mostly been restricted to a binary paradigm of gender. It is essential also to consider non-binary gender identities, as excluding them can cause further harm to an already marginalized group. In this paper, we comprehensively evaluate popular language models for their ability to correctly use English gender-neutral pronouns (e.g., singular they, them) and neo-pronouns (e.g., ze, xe, thon) that are used by individuals whose gender identity is not represented by binary pronouns. We introduce MISGENDERED, a framework for evaluating large language models' ability to correctly use preferred pronouns, consisting of (i) instances declaring an individual's pronoun, followed by a sentence with a missing pronoun, and (ii) an experimental setup for evaluating masked and auto-regressive language models using a unified method. When prompted out-of-the-box, language models perform poorly at correctly predicting neo-pronouns (averaging 7.7% accuracy) and gender-neutral pronouns (averaging 34.2% accuracy). This inability to generalize results from a lack of representation of non-binary pronouns in training data and memorized associations. Few-shot adaptation with explicit examples in the prompt improves performance for neo-pronouns, but only to 64.7% even with 20 shots. We release the full dataset, code, and demo at https://tamannahossainkay.github.io/misgendered/

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 6 Jun 2023.

Builds on

23 of 42 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  9. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
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11 more
  1. 2019Evaluating Gender Bias in Machine Translationcited by 247
  2. 2021Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Modelscited by 141
  3. 2021Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologiescited by 128
  4. 2019Toward Gender-Inclusive Coreference Resolutioncited by 84
  5. 2023"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generationcited by 56
  6. 2022Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gendercited by 47
  7. 2020OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddingscited by 40
  8. 2022Quantifying Social Biases Using Templates is Unreliablecited by 38
  9. 2022The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarkscited by 31
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 19; 2025: 12; 2026: 9.

43 papers.

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  1. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  2. 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathwayscited by 122
  3. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  4. 2023VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolutioncited by 52
  5. 2024Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?cited by 24
  6. 2024Robust Pronoun Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased?cited by 17
  7. 2023Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologiescited by 16
  8. 2024WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Casecited by 9
  9. 2024From Descriptive Richness to Bias: Unveiling the Dark Side of Generative Image Caption Enrichmentcited by 8
  10. 2024How Well Do LLMs Identify Cultural Unity in Diversity?cited by 8
  11. 2024Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Modelscited by 7
  12. 2024MisgenderMender: A Community-Informed Approach to Interventions for Misgenderingcited by 6
31 more
  1. 2024Learning from Natural Language Explanations for Generalizable Entity Matchingcited by 6
  2. 2025Agree to Disagree? A Meta-Evaluation of LLM Misgenderingcited by 6
  3. 2024MiTTenS: A Dataset for Evaluating Gender Mistranslationcited by 5
  4. 2024QueerBench: Quantifying Discrimination in Language Models Toward Queer Identitiescited by 5
  5. 2024Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMscited by 5
  6. 2024The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classificationcited by 5
  7. 2024The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Modelscited by 5
  8. 2025Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Termscited by 5
  9. 2025LLM Bias Detection and Mitigation through the Lens of Desired Distributionscited by 4
  10. 2026Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decompositioncited by 4
  11. 2025Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolutioncited by 3
  12. 2024Transforming Dutch: Debiasing Dutch Coreference Resolution Systems for Non-binary Pronounscited by 2
  13. 2025An LLM-as-a-judge Approach for Scalable Gender-Neutral Translation Evaluationcited by 2
  14. 2025Understanding Gender Bias in AI-Generated Product Descriptionscited by 2
  15. 2024Exploring LGBTQ+ Bias in Generative AI Answers across Different Country and Religious Contextscited by 1
  16. 2024Probing Causality Manipulation of Large Language Modelscited by 1
  17. 2024Solving the Challenge Set without Solving the Task: On Winograd Schemas as a Test of Pronominal Coreference Resolutioncited by 1
  18. 2025Robust Bias Detection in MLMs and its Application to Human Trait Ratingscited by 1
  19. 2025Analyzing values about gendered language reform in LLMs' revisionscited by 1
  20. 2025SLAyiNG: A Diverse and Community-validated Dataset of Queer Slangcited by 1
  21. 2026QueerGen: How LLMs Reflect Societal Norms on Gender and Sexuality in Sentence Completion Taskscited by 1
  22. 2026Voice or Stereotype? Disentangling Acoustic and Content-Based Gender in Speech-to-Speech Modelscited by 1
  23. 2025Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in PubMed Abstractscited by 0
  24. 2025Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Modelscited by 0
  25. 2025Do They Understand Them? An Updated Evaluation on Nonbinary Pronoun Handling in Large Language Modelscited by 0
  26. 2026GRUFF: LLM Pronoun Fidelity, Reasoning, and Biases in Germancited by 0
  27. 2026Neutrality Bites: Gender Representation in AI-Generated Animal Storiescited by 0
  28. 2026On Defining Erasure Harms for NLPcited by 0
  29. 2026A Mechanistic Understanding of Pronoun Fidelity in LLMscited by 0
  30. 2026How Do Large Language Models Judge Social Attraction? Evidence from Theory-Grounded Persona Ratings Across Multiple LLMs and Humanscited by 0
  31. 2026How Humans and LLMs Read Gender into "Gender-Neutral" Physical Descriptionscited by 0