Measuring Gender Bias in West Slavic Language Models
- URL: http://arxiv.org/abs/2304.05783v3
- Date: Thu, 25 May 2023 08:51:47 GMT
- Title: Measuring Gender Bias in West Slavic Language Models
- Authors: Sandra Martinkov\'a, Karolina Sta\'nczak, Isabelle Augenstein
- Abstract summary: We introduce the first template-based dataset in Czech, Polish, and Slovak for measuring gender bias towards male, female and non-binary subjects.
We measure gender bias encoded in West Slavic language models by quantifying the toxicity and genderness of the generated words.
We find that these language models produce hurtful completions that depend on the subject's gender.
- Score: 41.49834421110596
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pre-trained language models have been known to perpetuate biases from the
underlying datasets to downstream tasks. However, these findings are
predominantly based on monolingual language models for English, whereas there
are few investigative studies of biases encoded in language models for
languages beyond English. In this paper, we fill this gap by analysing gender
bias in West Slavic language models. We introduce the first template-based
dataset in Czech, Polish, and Slovak for measuring gender bias towards male,
female and non-binary subjects. We complete the sentences using both mono- and
multilingual language models and assess their suitability for the masked
language modelling objective. Next, we measure gender bias encoded in West
Slavic language models by quantifying the toxicity and genderness of the
generated words. We find that these language models produce hurtful completions
that depend on the subject's gender. Perhaps surprisingly, Czech, Slovak, and
Polish language models produce more hurtful completions with men as subjects,
which, upon inspection, we find is due to completions being related to
violence, death, and sickness.
Related papers
- Are Models Biased on Text without Gender-related Language? [14.931375031931386]
We introduce UnStereoEval (USE), a novel framework for investigating gender bias in stereotype-free scenarios.
USE defines a sentence-level score based on pretraining data statistics to determine if the sentence contain minimal word-gender associations.
We find low fairness across all 28 tested models, suggesting that bias does not solely stem from the presence of gender-related words.
arXiv Detail & Related papers (2024-05-01T15:51:15Z) - Investigating Gender Bias in Turkish Language Models [3.100560442806189]
We investigate the significance of gender bias in Turkish language models.
We build upon existing bias evaluation frameworks and extend them to the Turkish language.
Specifically, we evaluate Turkish language models for their embedded ethnic bias toward Kurdish people.
arXiv Detail & Related papers (2024-04-17T20:24:41Z) - Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You [64.74707085021858]
We show that multilingual models suffer from significant gender biases just as monolingual models do.
We propose a novel benchmark, MAGBIG, intended to foster research on gender bias in multilingual models.
Our results show that not only do models exhibit strong gender biases but they also behave differently across languages.
arXiv Detail & Related papers (2024-01-29T12:02:28Z) - Will the Prince Get True Love's Kiss? On the Model Sensitivity to Gender
Perturbation over Fairytale Texts [87.62403265382734]
Recent studies show that traditional fairytales are rife with harmful gender biases.
This work aims to assess learned biases of language models by evaluating their robustness against gender perturbations.
arXiv Detail & Related papers (2023-10-16T22:25:09Z) - Measuring Harmful Representations in Scandinavian Language Models [14.895663939509634]
We show that Scandinavian pre-trained language models contain harmful and gender-based stereotypes.
This finding goes against the general expectations related to gender equality in Scandinavian countries.
arXiv Detail & Related papers (2022-11-21T17:46:39Z) - Efficient Gender Debiasing of Pre-trained Indic Language Models [0.0]
The gender bias present in the data on which language models are pre-trained gets reflected in the systems that use these models.
In our paper, we measure gender bias associated with occupations in Hindi language models.
Our results reflect that the bias is reduced post-introduction of our proposed mitigation techniques.
arXiv Detail & Related papers (2022-09-08T09:15:58Z) - Do Multilingual Language Models Capture Differing Moral Norms? [71.52261949766101]
Massively multilingual sentence representations are trained on large corpora of uncurated data.
This may cause the models to grasp cultural values including moral judgments from the high-resource languages.
The lack of data in certain languages can also lead to developing random and thus potentially harmful beliefs.
arXiv Detail & Related papers (2022-03-18T12:26:37Z) - Quantifying Gender Bias Towards Politicians in Cross-Lingual Language
Models [104.41668491794974]
We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender.
We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians.
arXiv Detail & Related papers (2021-04-15T15:03:26Z) - 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.