Exploring and Mitigating Gender Bias in Encoder-Based Transformer Models

• URL: http://arxiv.org/abs/2511.00519v1
• Date: Sat, 01 Nov 2025 11:49:44 GMT
• Title: Exploring and Mitigating Gender Bias in Encoder-Based Transformer Models
• Authors: Ariyan Hossain, Khondokar Mohammad Ahanaf Hannan, Rakinul Haque, Nowreen Tarannum Rafa, Humayra Musarrat, Shoaib Ahmed Dipu, Farig Yousuf Sadeque,
• Abstract summary: This paper investigates gender bias in contextualized word embeddings, a crucial component of transformer-based models.<n>To quantify the degree of bias, we introduce a novel metric, MALoR, which assesses bias based on model probabilities for filling masked tokens.<n>Our experiments reveal significant reductions in gender bias scores across different pronoun pairs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gender bias in language models has gained increasing attention in the field of natural language processing. Encoder-based transformer models, which have achieved state-of-the-art performance in various language tasks, have been shown to exhibit strong gender biases inherited from their training data. This paper investigates gender bias in contextualized word embeddings, a crucial component of transformer-based models. We focus on prominent architectures such as BERT, ALBERT, RoBERTa, and DistilBERT to examine their vulnerability to gender bias. To quantify the degree of bias, we introduce a novel metric, MALoR, which assesses bias based on model probabilities for filling masked tokens. We further propose a mitigation approach involving continued pre-training on a gender-balanced dataset generated via Counterfactual Data Augmentation. Our experiments reveal significant reductions in gender bias scores across different pronoun pairs. For instance, in BERT-base, bias scores for "he-she" dropped from 1.27 to 0.08, and "his-her" from 2.51 to 0.36 following our mitigation approach. We also observed similar improvements across other models, with "male-female" bias decreasing from 1.82 to 0.10 in BERT-large. Our approach effectively reduces gender bias without compromising model performance on downstream tasks.

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