Quantifying gender bias towards politicians in cross-lingual language models.

ABSTRACT: Recent research has demonstrated that large pre-trained language models reflect societal biases expressed in natural language. The present paper introduces a simple method for probing language models to conduct a multilingual study of gender bias towards politicians. We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. To this end, we curate a dataset of 250k politicians worldwide, including their names and gender. Our study is conducted in seven languages across six different language modeling architectures. The results demonstrate that pre-trained language models' stance towards politicians varies strongly across analyzed languages. We find that while some words such as dead, and designated are asso

SUBMITTER: Stanczak K 

PROVIDER: S-EPMC10684026 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature