mlsanityData to 14 Oct 2026

Nichelle and Nancy: The Influence of Demographic Attributes and Tokenization Length on First Name Biases

arXiv 2305.16577 ↗First posted 26 May 2023cs.CLcited by 12

Not on the map. Why?

Abstract (arXiv)

Through the use of first name substitution experiments, prior research has demonstrated the tendency of social commonsense reasoning models to systematically exhibit social biases along the dimensions of race, ethnicity, and gender (An et al., 2023). Demographic attributes of first names, however, are strongly correlated with corpus frequency and tokenization length, which may influence model behavior independent of or in addition to demographic factors. In this paper, we conduct a new series of first name substitution experiments that measures the influence of these factors while controlling for the others. We find that demographic attributes of a name (race, ethnicity, and gender) and name tokenization length are both factors that systematically affect the behavior of social commonsense reasoning models.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

23 of 37 references.

References · 23

  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. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  4. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
  5. 2015Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Bookscited by 1,043
  6. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  7. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 924
  8. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 710
  9. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 701
  10. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 659
  11. 2018Gender Bias in Coreference Resolutioncited by 442
  12. 2019Social Bias Frames: Reasoning about Social and Power Implications of Languagecited by 380
11 more
  1. 2020Social Chemistry 101: Learning to Reason about Social and Moral Normscited by 255
  2. 2019Gender Bias in Contextualized Word Embeddingscited by 203
  3. 2020UnQovering Stereotyping Biases via Underspecified Questionscited by 126
  4. 2019It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitutioncited by 103
  5. 2021Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metricscited by 83
  6. 2022Perturbation Augmentation for Fairer NLPcited by 52
  7. 2020"You are grounded!": Latent Name Artifacts in Pre-trained Language Modelscited by 39
  8. 2021Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Modelscited by 39
  9. 2022Measuring Representational Harms in Image Captioningcited by 36
  10. 2022SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Modelscited by 22
  11. 2022Race and ethnicity data for first, middle, and last namescited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 5; 2026: 2.

12 papers.

  1. 2024From Understanding to Utilization: A Survey on Explainability for Large Language Modelscited by 48
  2. 2024Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?cited by 25
  3. 2025Agree to Disagree? A Meta-Evaluation of LLM Misgenderingcited by 7
  4. 2024The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language Modelscited by 6
  5. 2024A Study of Nationality Bias in Names and Perplexity using Off-the-Shelf Affect-related Tweet Classifierscited by 4
  6. 2025On the Mutual Influence of Gender and Occupation in LLM Representationscited by 4
  7. 2026How Does "English (US)" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipelinecited by 3
  8. 2025Talent or Luck? Evaluating Attribution Bias in Large Language Modelscited by 2
  9. 2025Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contextscited by 1
  10. 2024On the Influence of Gender and Race in Romantic Relationship Prediction from Large Language Modelscited by 0
  11. 2025Teaching and Critiquing Conceptualization and Operationalization in NLPcited by 0
  12. 2026Who Gets a Token, and What Does It Carry? Unequal Name Support and Concept Access in Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoning
  2. 2024Prompt and Prejudice
  3. 2024White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs
  4. 2025Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution
  5. 2025Evaluating the Effect of Retrieval Augmentation on Social Biases
  6. 2024Robustness and Confounders in the Demographic Alignment of LLMs with Human Perceptions of Offensiveness
  7. 2026Evaluating Social Bias in RAG Systems: When External Context Helps and Reasoning Hurts
  8. 2023Gender bias and stereotypes in Large Language Models