# Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications

arXiv: https://arxiv.org/abs/2307.09162 · HTML: https://mlsanity.com/paper/2307.09162/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 18 Jul 2023.

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

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018)
- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://mlsanity.com/paper/2305.18189/) (2023)
- [In-Context Impersonation Reveals Large Language Models' Strengths and Biases](https://mlsanity.com/paper/2305.14930/) (2023)
- [Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity](https://mlsanity.com/paper/2301.12867/) (2023)
- [Gender Bias in Masked Language Models for Multiple Languages](https://mlsanity.com/paper/2205.00551/) (2022)
- [Nationality Bias in Text Generation](https://mlsanity.com/paper/2302.02463/) (2023)
- [Evaluating GPT-3 Generated Explanations for Hateful Content Moderation](https://mlsanity.com/paper/2305.17680/) (2023)
- [Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal](https://mlsanity.com/paper/2203.12574/) (2022)
- [ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languages](https://mlsanity.com/paper/2305.10510/) (2023)
- [Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias](https://mlsanity.com/paper/2212.10678/) (2022)
- [Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions](https://mlsanity.com/paper/2306.04597/) (2023)
- [The Birth of Bias: A case study on the evolution of gender bias in an English language model](https://mlsanity.com/paper/2207.10245/) (2022)
- [She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Models](https://mlsanity.com/paper/2303.10131/) (2023)
- [Measuring Gender Bias in West Slavic Language Models](https://mlsanity.com/paper/2304.05783/) (2023)
- [Efficient Gender Debiasing of Pre-trained Indic Language Models](https://mlsanity.com/paper/2209.03661/) (2022)
- [Gender Bias in Transformer Models: A comprehensive survey](https://mlsanity.com/paper/2306.10530/) (2023)

## Built on by (most cited first, 29 of 29)

- [Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models](https://mlsanity.com/paper/2405.10431/) (2024)
- [D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models](https://mlsanity.com/paper/2406.01375/) (2024)
- [Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models](https://mlsanity.com/paper/2501.17420/) (2025)
- [DOSA: A Dataset of Social Artifacts from Different Indian Geographical Subcultures](https://mlsanity.com/paper/2403.14651/) (2024)
- [How susceptible are LLMs to Logical Fallacies?](https://mlsanity.com/paper/2308.09853/) (2023)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [Indian-BhED: A Dataset for Measuring India-Centric Biases in Large Language Models](https://mlsanity.com/paper/2309.08573/) (2023)
- [CIDAR: Culturally Relevant Instruction Dataset For Arabic](https://mlsanity.com/paper/2402.03177/) (2024)
- [AuditLLM: A Tool for Auditing Large Language Models Using Multiprobe Approach](https://mlsanity.com/paper/2402.09334/) (2024)
- [Guiding LLM Decision-Making with Fairness Reward Models](https://mlsanity.com/paper/2507.11344/) (2025)
- ['Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMs](https://mlsanity.com/paper/2409.13484/) (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)
- [Evaluating how LLM annotations represent diverse views on contentious topics](https://mlsanity.com/paper/2503.23243/) (2025)
- [Best Practices for Large Language Models in Radiology](https://mlsanity.com/paper/2412.01233/) (2024)
- [Building Benchmarks from the Ground Up: Community-Centered Evaluation of LLMs in Healthcare Chatbot Settings](https://mlsanity.com/paper/2509.24506/) (2025)
- [OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2402.13524/) (2024)
- ["Flex Tape Can't Fix That": Bias and Misinformation in Edited Language Models](https://mlsanity.com/paper/2403.00180/) (2024)
- [SafeLLM: Unlearning Harmful Outputs from Large Language Models against Jailbreak Attacks](https://mlsanity.com/paper/2508.15182/) (2025)
- [LLM Bias Evaluation: Gender, Racial, and Age Disparities in Occupational and Crime Scenarios](https://mlsanity.com/paper/2409.14583/) (2024)
- [From Bytes to Biases: Investigating the Cultural Self-Perception of Large Language Models](https://mlsanity.com/paper/2312.17256/) (2023)
- [Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigms](https://mlsanity.com/paper/2410.15212/) (2024)
- [The LLM Wears Prada: Analysing Gender Bias and Stereotypes through Online Shopping Data](https://mlsanity.com/paper/2504.01951/) (2025)
- [Evaluation of Large Language Models: STEM education and Gender Stereotypes](https://mlsanity.com/paper/2406.10133/) (2024)
- [Personalisation or Prejudice? Addressing Geographic Bias in Hate Speech Detection using Debias Tuning in Large Language Models](https://mlsanity.com/paper/2505.02252/) (2025)
- [Probing Gender Bias in Multilingual LLMs: A Case Study of Stereotypes in Persian](https://mlsanity.com/paper/2509.20168/) (2025)
- [Contaminated Collaboration: Measuring Gender Bias Transfer in LLM-Assisted Student Writing](https://mlsanity.com/paper/2606.15914/) (2026)
- [Symphony of Bias: Exploring Gender Associations with Musical Instruments in Multimodal LLMs](https://mlsanity.com/paper/2607.26355/) (2026)
- [Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level](https://mlsanity.com/paper/2608.14692/) (2026)

## Abstract (arXiv)

Gender bias in artificial intelligence (AI) and natural language processing has garnered significant attention due to its potential impact on societal perceptions and biases. This research paper aims to analyze gender bias in Large Language Models (LLMs) with a focus on multiple comparisons between GPT-2 and GPT-3.5, some prominent language models, to better understand its implications. Through a comprehensive literature review, the study examines existing research on gender bias in AI language models and identifies gaps in the current knowledge. The methodology involves collecting and preprocessing data from GPT-2 and GPT-3.5, and employing in-depth quantitative analysis techniques to evaluate gender bias in the generated text. The findings shed light on gendered word associations, language usage, and biased narratives present in the outputs of these Large Language Models. The discussion explores the ethical implications of gender bias and its potential consequences on social perceptions and marginalized communities. Additionally, the paper presents strategies for reducing gender bias in LLMs, including algorithmic approaches and data augmentation techniques. The research highlights the importance of interdisciplinary collaborations and the role of sociological studies in mitigating gender bias in AI models. By addressing these issues, we can pave the way for more inclusive and unbiased AI systems that have a positive impact on society.