Probing Gender Bias in Multilingual LLMs: A Case Study of Stereotypes in Persian
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Abstract (arXiv)
Multilingual Large Language Models (LLMs) are increasingly used worldwide, making it essential to ensure they are free from gender bias to prevent representational harm. While prior studies have examined such biases in high-resource languages, low-resource languages remain understudied. In this paper, we propose a template-based probing methodology, validated against real-world data, to uncover gender stereotypes in LLMs. As part of this framework, we introduce the Domain-Specific Gender Skew Index (DS-GSI), a metric that quantifies deviations from gender parity. We evaluate four prominent models, GPT-4o mini, DeepSeek R1, Gemini 2.0 Flash, and Qwen QwQ 32B, across four semantic domains, focusing on Persian, a low-resource language with distinct linguistic features. Our results show that all models exhibit gender stereotypes, with greater disparities in Persian than in English across all domains. Among these, sports reflect the most rigid gender biases. This study underscores the need for inclusive NLP practices and provides a framework for assessing bias in other low-resource languages.
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References · 6
- 2020Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpuscited by 57
- 2024Disclosure and Mitigation of Gender Bias in LLMscited by 49
- 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
- 2023Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implicationscited by 29
- 2024GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languagescited by 4
- 2025Mind the Language Gap: Automated and Augmented Evaluation of Bias in LLMs for High- and Low-Resource Languagescited by 4
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- 2025EuroGEST: Investigating gender stereotypes in multilingual language models
- 2023Gender bias and stereotypes in Large Language Models
- 2025Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations
- 2025Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective
- 2025More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models
- 2025Unsupervised Concept Vector Extraction for Bias Control in LLMs
- 2024Interpreting Bias in Large Language Models: A Feature-Based Approach
- 2023Women Are Beautiful, Men Are Leaders: Gender Stereotypes in Machine Translation and Language Modeling