Investigating Markers and Drivers of Gender Bias in Machine Translations
(2023)
Conference Proceeding
Barclay, P., & Sami, A. (in press). Investigating Markers and Drivers of Gender Bias in Machine Translations. In IEEE International Conference on Software Analysis, Evolution and Reengineering
Implicit gender bias in Large Language Models (LLMs) is a well-documented problem, and implications of gender introduced into automatic translations can perpetuate real-world biases. However, some LLMs use heuristics or post-processing to mask such b... Read More about Investigating Markers and Drivers of Gender Bias in Machine Translations.