Investigating Markers and Drivers of Gender Bias in Machine Translations

arXiv:2403.11896v1 Announce Type: new
Abstract: 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 bias, making investigation difficult. Here, we examine bias in LLMss via back-translation, using the DeepL translation API to investigate the bias evinced when repeatedly translating a set of 56 Software Engineering tasks used in a previous study. Each statement starts with …

abstract arxiv bias biases cs.cl cs.cy cs.se drivers gender gender bias heuristics however investigation language language models large language large language models llms machine making post-processing processing type via world