Leveraging Large Language Models to Measure Gender Bias in Gendered Languages

• URL: http://arxiv.org/abs/2406.13677v1
• Date: Wed, 19 Jun 2024 16:30:58 GMT
• Title: Leveraging Large Language Models to Measure Gender Bias in Gendered Languages
• Authors: Erik Derner, Sara Sansalvador de la Fuente, Yoan Gutiérrez, Paloma Moreda, Nuria Oliver,
• Abstract summary: This paper introduces a novel methodology that leverages the contextual understanding capabilities of large language models (LLMs) to quantitatively analyze gender representation in Spanish corpora. We empirically validate our method on four widely-used benchmark datasets, uncovering significant gender disparities with a male-to-female ratio ranging from 4:01.
• Score: 9.959039325564744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classification [57.06913662622832]
  Gender-fair language fosters inclusion by addressing all genders or using neutral forms. Gender-fair language substantially impacts predictions by flipping labels, reducing certainty, and altering attention patterns. While we offer initial insights on the effect on German text classification, the findings likely apply to other languages.
  arXiv  Detail & Related papers  (2024-09-26T15:08:17Z)
• GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
  Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases. GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
  arXiv  Detail & Related papers  (2024-08-22T15:35:46Z)
• Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
  Existing machine translation gender bias evaluations are primarily focused on male and female genders. This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words) We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
  arXiv  Detail & Related papers  (2024-07-23T08:13:51Z)
• From 'Showgirls' to 'Performers': Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMs [1.1049608786515839]
  We adapt linguistic structures within Large Language Models to promote gender-inclusivity. The focus of our work is gender-exclusive affixes in English, such as in'show-girl' or'man-cave'
  arXiv  Detail & Related papers  (2024-07-05T11:31:30Z)
• What is Your Favorite Gender, MLM? Gender Bias Evaluation in Multilingual Masked Language Models [8.618945530676614]
  This paper proposes an approach to estimate gender bias in multilingual lexicons from 5 languages: Chinese, English, German, Portuguese, and Spanish. A novel model-based method is presented to generate sentence pairs for a more robust analysis of gender bias. Our results suggest that gender bias should be studied on a large dataset using multiple evaluation metrics for best practice.
  arXiv  Detail & Related papers  (2024-04-09T21:12:08Z)
• Gender Bias in Large Language Models across Multiple Languages [10.068466432117113]
  We examine gender bias in large language models (LLMs) generated for different languages. We use three measurements: 1) gender bias in selecting descriptive words given the gender-related context. 2) gender bias in selecting gender-related pronouns (she/he) given the descriptive words.
  arXiv  Detail & Related papers  (2024-03-01T04:47:16Z)
• Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation [28.471506840241602]
  Gender bias is a significant issue in machine translation, leading to ongoing research efforts in developing bias mitigation techniques. We propose a bias mitigation method based on a novel approach. Gender-Aware Contrastive Learning, GACL, encodes contextual gender information into the representations of non-explicit gender words.
  arXiv  Detail & Related papers  (2023-05-23T12:53:39Z)
• INCLUSIFY: A benchmark and a model for gender-inclusive German [0.0]
  Gender-inclusive language is important for achieving gender equality in languages with gender inflections. A handful of tools have been developed to help people use gender-inclusive language. We present a dataset and measures for benchmarking them, and present a model that implements these tasks.
  arXiv  Detail & Related papers  (2022-12-05T19:37:48Z)
• Analyzing Gender Representation in Multilingual Models [59.21915055702203]
  We focus on the representation of gender distinctions as a practical case study. We examine the extent to which the gender concept is encoded in shared subspaces across different languages.
  arXiv  Detail & Related papers  (2022-04-20T00:13:01Z)
• Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer [101.58431011820755]
  We study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations.
  arXiv  Detail & Related papers  (2020-05-02T04:34:37Z)
• Multi-Dimensional Gender Bias Classification [67.65551687580552]
  Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
  arXiv  Detail & Related papers  (2020-05-01T21:23:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.