mlsanityData to 7 Oct 2026

Evaluating LLMs for Gender Disparities in Notable Persons

arXiv 2403.09148 ↗First posted 14 Mar 2024cs.CL cs.IRcited by 3

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Abstract (arXiv)

This study examines the use of Large Language Models (LLMs) for retrieving factual information, addressing concerns over their propensity to produce factually incorrect "hallucinated" responses or to altogether decline to even answer prompt at all. Specifically, it investigates the presence of gender-based biases in LLMs' responses to factual inquiries. This paper takes a multi-pronged approach to evaluating GPT models by evaluating fairness across multiple dimensions of recall, hallucinations and declinations. Our findings reveal discernible gender disparities in the responses generated by GPT-3.5. While advancements in GPT-4 have led to improvements in performance, they have not fully eradicated these gender disparities, notably in instances where responses are declined. The study further explores the origins of these disparities by examining the influence of gender associations in prompts and the homogeneity in the responses.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 14 Mar 2024.

Builds on

24 of 37 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  4. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  5. 2019How Can We Know What Language Models Know?cited by 953
  6. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
  7. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  8. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  9. 2021BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generationcited by 410
  10. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  11. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 376
  12. 2021A Survey on Automated Fact-Checkingcited by 376
12 more
  1. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 285
  2. 2023Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Studycited by 203
  3. 2023"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letterscited by 196
  4. 2020Fairness in Machine Learningcited by 194
  5. 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 183
  6. 2020Social Biases in NLP Models as Barriers for Persons with Disabilitiescited by 182
  7. 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
  8. 2018The Frontiers of Fairness in Machine Learningcited by 139
  9. 2018Racial categories in machine learningcited by 48
  10. 2023Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generationcited by 31
  11. 2023Measuring and Modifying Factual Knowledge in Large Language Modelscited by 16
  12. 2021What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Modelscited by 12

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictionscited by 17
  2. 2025To Mask or to Mirror: Human-AI Alignment in Collective Reasoningcited by 5
  3. 2025LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Modelscited by 2