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arXiv · 2407.18786

The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs

Aleix Sant·Carlos Escolano·Audrey Mash·Francesca De Luca Fornaciari·Maite Melero
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Abstract

This paper studies gender bias in machine translation through the lens of Large Language Models (LLMs). Four widely-used test sets are employed to benchmark various base LLMs, comparing their translation quality and gender bias against state-of-the-art Neural Machine Translation (NMT) models for English to Catalan (En $\rightarrow$ Ca) and English to Spanish (En $\rightarrow$ Es) translation directions. Our findings reveal pervasive gender bias across all models, with base LLMs exhibiting a higher degree of bias compared to NMT models. To combat this bias, we explore prompting engineering techniques applied to an instruction-tuned LLM. We identify a prompt structure that significantly reduces gender bias by up to 12% on the WinoMT evaluation dataset compared to more straightforward prompts. These results significantly reduce the gender bias accuracy gap between LLMs and traditional NMT systems.

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Aleix Sant, Carlos Escolano, Audrey Mash, Francesca De Luca Fornaciari, Maite Melero. 2024-07-26. The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs. https://arxiv.org/abs/2407.18786

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