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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 → Ca) and English to Spanish (En → 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.

ACL 2024

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

prompting engineering

gender bias

large language models

machine translation

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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What Can Go Wrong in Authorship Profiling: Cross-Domain Analysis of Gender and Age Prediction

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

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What Can Go Wrong in Authorship Profiling: Cross-Domain Analysis of Gender and Age Prediction

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