# Disclosure and Mitigation of Gender Bias in LLMs

arXiv: https://arxiv.org/abs/2402.11190 · HTML: https://mlsanity.com/paper/2402.11190/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [Red Teaming Language Models with Language Models](https://mlsanity.com/paper/2202.03286/) (2022)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018)
- [BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation](https://mlsanity.com/paper/2101.11718/) (2021)
- [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://mlsanity.com/paper/1901.09451/) (2019)
- [Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP](https://mlsanity.com/paper/2103.00453/) (2021)
- [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://mlsanity.com/paper/2212.08061/) (2022)
- [Towards Debiasing Sentence Representations](https://mlsanity.com/paper/2007.08100/) (2020)
- [Reducing Sentiment Bias in Language Models via Counterfactual Evaluation](https://mlsanity.com/paper/1911.03064/) (2019)
- [Towards Controllable Biases in Language Generation](https://mlsanity.com/paper/2005.00268/) (2020)
- [Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model](https://mlsanity.com/paper/2305.11140/) (2023)

## Built on by (most cited first, 30 of 49)

- [A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment](https://mlsanity.com/paper/2504.15585/) (2025)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- ["You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations](https://mlsanity.com/paper/2406.12232/) (2024)
- [When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn Settings](https://mlsanity.com/paper/2407.04503/) (2024)
- [A Survey on LLM Inference-Time Self-Improvement](https://mlsanity.com/paper/2412.14352/) (2024)
- [Social Bias in Large Language Models For Bangla: An Empirical Study on Gender and Religious Bias](https://mlsanity.com/paper/2407.03536/) (2024)
- [DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis](https://mlsanity.com/paper/2505.14971/) (2025)
- [LLMs for Customized Marketing Content Generation and Evaluation at Scale](https://mlsanity.com/paper/2506.17863/) (2025)
- [Are Large Language Models Chameleons? An Attempt to Simulate Social Surveys](https://mlsanity.com/paper/2405.19323/) (2024)
- [Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey](https://mlsanity.com/paper/2408.04643/) (2024)
- ['Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMs](https://mlsanity.com/paper/2409.13484/) (2024)
- [Measuring Bias or Measuring the Task: Understanding the Brittle Nature of LLM Gender Biases](https://mlsanity.com/paper/2509.04373/) (2025)
- [CLIMB: A Benchmark of Clinical Bias in Large Language Models](https://mlsanity.com/paper/2407.05250/) (2024)
- [Rethinking LLM Bias Probing Using Lessons from the Social Sciences](https://mlsanity.com/paper/2503.00093/) (2025)
- [Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction](https://mlsanity.com/paper/2406.08183/) (2024)
- [Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI](https://mlsanity.com/paper/2410.15467/) (2024)
- [An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models](https://mlsanity.com/paper/2407.06432/) (2024)
- [The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models](https://mlsanity.com/paper/2411.03700/) (2024)
- [How far can bias go? Tracing bias from pretraining data to alignment](https://mlsanity.com/paper/2411.19240/) (2024)
- [The World According to LLMs: How Geographic Origin Influences LLMs' Entity Deduction Capabilities](https://mlsanity.com/paper/2508.05525/) (2025)
- [Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models](https://mlsanity.com/paper/2406.05602/) (2024)
- [Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs](https://mlsanity.com/paper/2505.17217/) (2025)
- [Local Contrastive Editing of Gender Stereotypes](https://mlsanity.com/paper/2410.17739/) (2024)
- [From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper](https://mlsanity.com/paper/2503.07450/) (2025)
- [Gender and content bias in Large Language Models: a case study on Google Gemini 2.0 Flash Experimental](https://mlsanity.com/paper/2503.16534/) (2025)
- [LLM Bias Evaluation: Gender, Racial, and Age Disparities in Occupational and Crime Scenarios](https://mlsanity.com/paper/2409.14583/) (2024)
- [LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models](https://mlsanity.com/paper/2505.15475/) (2025)
- [Relative Bias: A Comparative Framework for Quantifying Bias in LLMs](https://mlsanity.com/paper/2505.17131/) (2025)
- [Do LLMs have a Gender (Entropy) Bias?](https://mlsanity.com/paper/2505.20343/) (2025)
- [An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents](https://mlsanity.com/paper/2602.03775/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) can generate biased responses. Yet previous direct probing techniques contain either gender mentions or predefined gender stereotypes, which are challenging to comprehensively collect. Hence, we propose an indirect probing framework based on conditional generation. This approach aims to induce LLMs to disclose their gender bias even without explicit gender or stereotype mentions. We explore three distinct strategies to disclose explicit and implicit gender bias in LLMs. Our experiments demonstrate that all tested LLMs exhibit explicit and/or implicit gender bias, even when gender stereotypes are not present in the inputs. In addition, an increased model size or model alignment amplifies bias in most cases. Furthermore, we investigate three methods to mitigate bias in LLMs via Hyperparameter Tuning, Instruction Guiding, and Debias Tuning. Remarkably, these methods prove effective even in the absence of explicit genders or stereotypes.