More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models
This addresses fairness issues in AI for users and developers, but is incremental as it builds on existing bias research.
The study tackled gender bias in large language models by introducing a free-form storytelling evaluation framework, revealing that while female characters are overrepresented in occupations, the models' outputs still align more with stereotypes than real-world data.
Large Language Models (LLMs) have revolutionized natural language processing, yet concerns persist regarding their tendency to reflect or amplify social biases. This study introduces a novel evaluation framework to uncover gender biases in LLMs: using free-form storytelling to surface biases embedded within the models. A systematic analysis of ten prominent LLMs shows a consistent pattern of overrepresenting female characters across occupations, likely due to supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). Paradoxically, despite this overrepresentation, the occupational gender distributions produced by these LLMs align more closely with human stereotypes than with real-world labor data. This highlights the challenge and importance of implementing balanced mitigation measures to promote fairness and prevent the establishment of potentially new biases. We release the prompts and LLM-generated stories at GitHub.