Gender bias in large language models (LLMs) is analyzed across multiple languages. The study explores biases in descriptive word selection, gendered role selection, and dialogue topics. Significant biases are identified, emphasizing the importance of addressing and mitigating these biases for more equitable and culturally aware LLM applications.
The study investigates gender bias in LLM-generated outputs for different languages using three measurements: descriptive word selection bias, gendered role selection bias, and bias in dialogue topics. Findings reveal significant gender biases across all examined languages.
Previous studies have focused on single-language evaluations of gender bias in language models. This work expands the analysis to multiple languages, providing insights into the variations of gender biases present in LLMs.
The research highlights the need to address and mitigate gender biases in LLMs to ensure fairness and cultural awareness across diverse applications and user backgrounds.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Jinman Zhao,... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00277.pdfDeeper Inquiries