Gender Bias in Machine Translation and The Era of Large Language Models

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本文分析了机器翻译系统如何在不同语言环境中延续性别偏见,特别关注了ChatGPT在英意翻译中的表现。研究发现,尽管有意识地提示提供性别包容的翻译,但GPT模型仍可能强化刻板印象。强调了消除MT系统偏见、促进公平和包容性的重要性,倡导多学科合作来解决这一问题。

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本文是LLM系列文章,针对《Gender Bias in Machine Translation and The Era of Large Language Models》的翻译。

机器翻译与大语言模型时代中的性别偏见

摘要

本章探讨了机器翻译在延续性别偏见方面的作用,强调了跨语言环境和统计依赖性带来的挑战。全面概述了传统神经机器翻译方法和作为机器翻译系统使用的生成预训练Transformer模型中与性别偏见相关的现有工作。通过在英意翻译环境中使用ChatGPT(基于GPT-3.5)的实验,我们进一步评估了ChatGPT目前解决性别偏见的能力。研究结果强调了在减少机器翻译系统中的偏见方面不断进步的必要性,并强调了在语言技术中促进公平和包容性的重要性。

1 引言

2 MT中的性别偏见

3 相关工作

4 前言实验和分析

5 结论

最后,本章深入探讨了MT在延续和塑造社会偏见,特别是性别偏见方面的关键作用。在MT领域,由于需要在特定的对比语言环境中消除歧义和明确表示性别,性别偏见尤其明显。在当前的机器翻译系统中,对有偏见的数据和统计模式的依赖可能导致错误的歧义消除,导致产生形态上不正确的替代品,强化潜在的有害刻板印象。
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