Gender bias in text corpora used in various natural language processing (NLP)
contexts, such as for training large language models (LLMs), can lead to the
perpetuation and amplification of societal inequalities. This is particularly
pronounced in gendered languages like Spanish or French, where grammatical
structures inherently encode gender, making the bias analysis more challenging.
Existing methods designed for English are inadequate for this task due to the
intrinsic linguistic differences between English and gendered languages. This
paper introduces a novel methodology that leverages the contextual
understanding capabilities of LLMs to quantitatively analyze gender
representation in Spanish corpora. By utilizing LLMs to identify and classify
gendered nouns and pronouns in relation to their reference to human entities,
our approach provides a nuanced analysis of gender biases. We empirically
validate our method on four widely-used benchmark datasets, uncovering
significant gender disparities with a male-to-female ratio ranging from 4:1 to
6:1. These findings demonstrate the value of our methodology for bias
quantification in gendered languages and suggest its application in NLP,
contributing to the development of more equitable language technologies.

该论文介绍了一种利用大型语言模型对西班牙语语料库进行定量分析性别表达的新方法，通过识别和分类与人类实体相关的具有性别特征的名词和代词，揭示了四个广泛使用的基准数据集中存在的显著性别差异，从男性到女性的比例范围从 4:1 到 6:1，这些发现证明了我们方法在性别语言中进行偏见量化的价值，并建议在自然语言处理中应用该方法，为更公平的语言技术发展做出贡献。

利用大型语言模型衡量性别化语言中的性别偏见

Leveraging Large Language Models to Measure Gender Bias in Gendered  Languages

With language models being deployed increasingly in the real world, it is
essential to address the issue of the fairness of their outputs. The word
embedding representations of these language models often implicitly draw
unwanted associations that form a social bias within the model. The nature of
gendered languages like Hindi, poses an additional problem to the
quantification and mitigation of bias, owing to the change in the form of the
words in the sentence, based on the gender of the subject. Additionally, there
is sparse work done in the realm of measuring and debiasing systems for Indic
languages. In our work, we attempt to evaluate and quantify the gender bias
within a Hindi-English machine translation system. We implement a modified
version of the existing TGBI metric based on the grammatical considerations for
Hindi. We also compare and contrast the resulting bias measurements across
multiple metrics for pre-trained embeddings and the ones learned by our machine
translation model.

本研究旨在评估和量化印地 - 英语机器翻译系统中的性别偏见，并实现了一种基于语法考虑的修改版本的现有 TGBI 度量标准，同时比较了预先训练的嵌入和我们的机器翻译模型所学习的嵌入的多个指标上产生的偏差测量结果。