# Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictions

arXiv: https://arxiv.org/abs/2407.05271 · HTML: https://mlsanity.com/paper/2407.05271/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts](https://mlsanity.com/paper/2306.04528/) (2023)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [CharBERT: Character-aware Pre-trained Language Model](https://mlsanity.com/paper/2011.01513/) (2020)
- [Assessing Demographic Bias in Named Entity Recognition](https://mlsanity.com/paper/2008.03415/) (2020)
- [Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency](https://mlsanity.com/paper/2105.08667/) (2021)
- [Gender Bias in BERT -- Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task](https://mlsanity.com/paper/2306.15298/) (2023)
- [What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models](https://mlsanity.com/paper/2102.03692/) (2021)
- [Citation inequity and gendered citation practices in contemporary physics](https://mlsanity.com/paper/2112.09047/) (2021)
- [Inferring gender from name: a large scale performance evaluation study](https://mlsanity.com/paper/2308.12381/) (2023)
- [Evaluating LLMs for Gender Disparities in Notable Persons](https://mlsanity.com/paper/2403.09148/) (2024)
- [Gender Prediction Based on Vietnamese Names with Machine Learning Techniques](https://mlsanity.com/paper/2010.10852/) (2020)
- [Gender inference: can chatGPT outperform common commercial tools?](https://mlsanity.com/paper/2312.00805/) (2023)
- [Gender inequality and self-publication patterns among scientific editors](https://mlsanity.com/paper/2207.01518/) (2022)
- [Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names](https://mlsanity.com/paper/2311.11001/) (2023)

## Built on by (most cited first, 17 of 17)

- [LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education](https://mlsanity.com/paper/2410.14012/) (2024)
- [Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM](https://mlsanity.com/paper/2508.05775/) (2025)
- [SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics](https://mlsanity.com/paper/2410.01946/) (2024)
- [On the Mutual Influence of Gender and Occupation in LLM Representations](https://mlsanity.com/paper/2503.06792/) (2025)
- [PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization](https://mlsanity.com/paper/2503.08890/) (2025)
- [No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language Models](https://mlsanity.com/paper/2503.11985/) (2025)
- [Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks](https://mlsanity.com/paper/2603.05801/) (2026)
- [LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models](https://mlsanity.com/paper/2505.15475/) (2025)
- [Investigating Gender Bias in LLM-Generated Stories via Psychological Stereotypes](https://mlsanity.com/paper/2508.03292/) (2025)
- [Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback](https://mlsanity.com/paper/2603.12471/) (2026)
- [Neuron-Level Interventions for Gendered and Gender-Neutral Generation in Language Models](https://mlsanity.com/paper/2605.30717/) (2026)
- [Man Made Language Models? Evaluating LLMs' Perpetuation of Masculine Generics Bias](https://mlsanity.com/paper/2502.10577/) (2025)
- [Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models](https://mlsanity.com/paper/2506.15568/) (2025)
- [Exploring Implicit Perspectives on Autism in Large Language Models Through Multi-Agent Simulations](https://mlsanity.com/paper/2601.15437/) (2026)
- [Symphony of Bias: Exploring Gender Associations with Musical Instruments in Multimodal LLMs](https://mlsanity.com/paper/2607.26355/) (2026)
- [LLMs Mirror Country-Specific Gender Patterns If Asked, but Skew Male When Generating Media in Local Languages](https://mlsanity.com/paper/2609.06545/) (2026)
- [How Humans and LLMs Read Gender into "Gender-Neutral" Physical Descriptions](https://mlsanity.com/paper/2609.16366/) (2026)

## Abstract (arXiv)

Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can be problematic in the cases of gender-neutral names that do not align with any one gender, among other reasons. Relying solely on binary gender categories without recognizing gender-neutral names can reduce the inclusiveness of gender prediction tasks. We introduce an additional gender category, i.e., "neutral", to study and address potential gender biases in Large Language Models (LLMs). We evaluate the performance of several foundational and large language models in predicting gender based on first names only. Additionally, we investigate the impact of adding birth years to enhance the accuracy of gender prediction, accounting for shifting associations between names and genders over time. Our findings indicate that most LLMs identify male and female names with high accuracy (over 80%) but struggle with gender-neutral names (under 40%), and the accuracy of gender prediction is higher for English-based first names than non-English names. The experimental results show that incorporating the birth year does not improve the overall accuracy of gender prediction, especially for names with evolving gender associations. We recommend using caution when applying LLMs for gender identification in downstream tasks, particularly when dealing with non-binary gender labels.