@inproceedings{ding-etal-2025-gender,
title = "Gender Bias in Large Language Models across Multiple Languages: A Case Study of {C}hat{GPT}",
author = "Ding, YiTian and
Zhao, Jinman and
Jia, Chen and
Wang, Yining and
Qian, Zifan and
Chen, Weizhe and
Yue, Xingyu",
editor = "Cao, Trista and
Das, Anubrata and
Kumarage, Tharindu and
Wan, Yixin and
Krishna, Satyapriya and
Mehrabi, Ninareh and
Dhamala, Jwala and
Ramakrishna, Anil and
Galystan, Aram and
Kumar, Anoop and
Gupta, Rahul and
Chang, Kai-Wei",
booktitle = "Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.trustnlp-main.36/",
doi = "10.18653/v1/2025.trustnlp-main.36",
pages = "552--579",
ISBN = "979-8-89176-233-6",
abstract = "With the growing deployment of large language models (LLMs) across various applications, assessing the influence of gender biases embedded in LLMs becomes crucial. The topic of gender bias within the realm of natural language processing (NLP) has gained considerable focus, particularly in the context of English. Nonetheless, the investigation of gender bias in languages other than English is still relatively under-explored and insufficiently analyzed. In this work, We examine gender bias in LLMs-generated outputs 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. 3) gender bias in the topics of LLM-generated dialogues. We investigate the outputs of the GPT series of LLMs in various languages using our three measurement methods. Our findings revealed significant gender biases across all the languages we examined."
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%0 Conference Proceedings
%T Gender Bias in Large Language Models across Multiple Languages: A Case Study of ChatGPT
%A Ding, YiTian
%A Zhao, Jinman
%A Jia, Chen
%A Wang, Yining
%A Qian, Zifan
%A Chen, Weizhe
%A Yue, Xingyu
%Y Cao, Trista
%Y Das, Anubrata
%Y Kumarage, Tharindu
%Y Wan, Yixin
%Y Krishna, Satyapriya
%Y Mehrabi, Ninareh
%Y Dhamala, Jwala
%Y Ramakrishna, Anil
%Y Galystan, Aram
%Y Kumar, Anoop
%Y Gupta, Rahul
%Y Chang, Kai-Wei
%S Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-233-6
%F ding-etal-2025-gender
%X With the growing deployment of large language models (LLMs) across various applications, assessing the influence of gender biases embedded in LLMs becomes crucial. The topic of gender bias within the realm of natural language processing (NLP) has gained considerable focus, particularly in the context of English. Nonetheless, the investigation of gender bias in languages other than English is still relatively under-explored and insufficiently analyzed. In this work, We examine gender bias in LLMs-generated outputs 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. 3) gender bias in the topics of LLM-generated dialogues. We investigate the outputs of the GPT series of LLMs in various languages using our three measurement methods. Our findings revealed significant gender biases across all the languages we examined.
%R 10.18653/v1/2025.trustnlp-main.36
%U https://aclanthology.org/2025.trustnlp-main.36/
%U https://doi.org/10.18653/v1/2025.trustnlp-main.36
%P 552-579
Markdown (Informal)
[Gender Bias in Large Language Models across Multiple Languages: A Case Study of ChatGPT](https://aclanthology.org/2025.trustnlp-main.36/) (Ding et al., TrustNLP 2025)
ACL