# Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation

arXiv: https://arxiv.org/abs/2311.00306 · HTML: https://mlsanity.com/paper/2311.00306/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 16 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [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)
- [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)
- [Identifying and Reducing Gender Bias in Word-Level Language Models](https://mlsanity.com/paper/1904.03035/) (2019)
- [Societal Biases in Language Generation: Progress and Challenges](https://mlsanity.com/paper/2105.04054/) (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)
- [An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models](https://mlsanity.com/paper/2110.08527/) (2021)
- [Co2PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning](https://mlsanity.com/paper/2310.12490/) (2023)

## Built on by (most cited first, 30 of 31)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Representation Engineering for Large-Language Models: Survey and Research Challenges](https://mlsanity.com/paper/2502.17601/) (2025)
- [CIDAR: Culturally Relevant Instruction Dataset For Arabic](https://mlsanity.com/paper/2402.03177/) (2024)
- [Unmasking Conversational Bias in AI Multiagent Systems](https://mlsanity.com/paper/2501.14844/) (2025)
- [Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflicts](https://mlsanity.com/paper/2410.11084/) (2024)
- [GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation](https://mlsanity.com/paper/2410.08475/) (2024)
- [DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis](https://mlsanity.com/paper/2505.14971/) (2025)
- [From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models](https://mlsanity.com/paper/2311.13063/) (2023)
- [What Do Llamas Really Think? Revealing Preference Biases in Language Model Representations](https://mlsanity.com/paper/2311.18812/) (2023)
- [Implicit Bias in LLMs: A Survey](https://mlsanity.com/paper/2503.02776/) (2025)
- [Measuring Bias or Measuring the Task: Understanding the Brittle Nature of LLM Gender Biases](https://mlsanity.com/paper/2509.04373/) (2025)
- [Learning Human-like Representations to Enable Learning Human Values](https://mlsanity.com/paper/2312.14106/) (2023)
- [CLIMB: A Benchmark of Clinical Bias in Large Language Models](https://mlsanity.com/paper/2407.05250/) (2024)
- [Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction](https://mlsanity.com/paper/2406.08183/) (2024)
- [ATACompressor: Adaptive Task-Aware Compression for Efficient Long-Context Processing in LLMs](https://mlsanity.com/paper/2602.03226/) (2026)
- [Steering LLMs for Formal Theorem Proving](https://mlsanity.com/paper/2502.15507/) (2025)
- [Probing Neural Topology of Large Language Models](https://mlsanity.com/paper/2506.01042/) (2025)
- [Evaluating LLMs for Gender Disparities in Notable Persons](https://mlsanity.com/paper/2403.09148/) (2024)
- [KnowPath: Knowledge-enhanced Reasoning via LLM-generated Inference Paths over Knowledge Graphs](https://mlsanity.com/paper/2502.12029/) (2025)
- [STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions](https://mlsanity.com/paper/2409.13843/) (2024)
- [Whose Journey Matters? Investigating Identity Biases in Large Language Models (LLMs) for Travel Planning Assistance](https://mlsanity.com/paper/2410.17333/) (2024)
- [Evaluating Gender Bias in Large Language Models](https://mlsanity.com/paper/2411.09826/) (2024)
- [DIF: A Framework for Benchmarking and Verifying Implicit Bias in LLMs](https://mlsanity.com/paper/2505.10013/) (2025)
- [Neuron-Level Interventions for Gendered and Gender-Neutral Generation in Language Models](https://mlsanity.com/paper/2605.30717/) (2026)
- [Implicit Priors Editing in Stable Diffusion via Targeted Token Adjustment](https://mlsanity.com/paper/2412.03400/) (2024)
- [Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager](https://mlsanity.com/paper/2604.00011/) (2026)
- [Redirected, Not Removed: Task-Dependent Stereotyping Reveals the Limits of LLM Alignments](https://mlsanity.com/paper/2604.02669/) (2026)
- [Gender Inequality in English Textbooks Around the World: an NLP Approach](https://mlsanity.com/paper/2506.02425/) (2025)
- [TransportAgents: a multi-agents LLM framework for traffic accident severity prediction](https://mlsanity.com/paper/2601.15519/) (2026)
- [Explanatory Interactive Machine Learning for Bias Mitigation in Visual Gender Classification](https://mlsanity.com/paper/2602.13286/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) can generate biased and toxic responses. Yet most prior work on LLM gender bias evaluation requires predefined gender-related phrases or gender stereotypes, which are challenging to be comprehensively collected and are limited to explicit bias evaluation. In addition, we believe that instances devoid of gender-related language or explicit stereotypes in inputs can still induce gender bias in LLMs. Thus, in this work, we propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes. This approach employs three types of inputs generated through three distinct strategies to probe LLMs, aiming to show evidence of explicit and implicit gender biases in LLMs. We also utilize explicit and implicit evaluation metrics to evaluate gender bias in LLMs under different strategies. Our experiments demonstrate that an increased model size does not consistently lead to enhanced fairness and all tested LLMs exhibit explicit and/or implicit gender bias, even when explicit gender stereotypes are absent in the inputs.