Gender Bias Analysis for Different Large Language Models

Large language models (LLMs) have transformed NLP applications across various domains, while raising crucial fairness and ethical concerns, especially regarding gender bias. This study examines gender bias in four leading LLMs, GPT-4o, Gemini 1.5 Pro, Sonnet 3.5, and LLaMA 3.1:8b—by evaluating the gender distributions these models generate for "perfect personas" across diverse occupational roles, including healthcare, engineering, and professional services. Using standardized prompts, controlled experimental settings, and repeated trials, we systematically quantify gender representation against a uniform baseline. The results reveal stark contrasts between models: GPT-4o exhibited pronounced occupational gender segregation, overwhelmingly assigning healthcare roles to females while reserving engineering and physically demanding roles for males. In contrast, Gemini 1.5 Pro, Sonnet 3.5, and LLaMA 3.1:8b showed predominantly high female assignments across a broad spectrum of occupations, with no comparable specificity in job-level representation. These findings underscore how architectural choices, training data composition, and token embedding strategies can amplify or mitigate biases, often at the expense of inclusivity. This research highlights the pressing need for designing inclusive datasets, implementing advanced bias-mitigation techniques, and conducting rigorous audits to ensure LLMs not only avoid perpetuating stereotypes but actively contribute to equitable and representative AI systems.