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Gender Bias in Large Language Models across Multiple Languages

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arxiv 2403.00277 v1 pith:ZTSZYOH2 submitted 2024-03-01 cs.CL

Jinman Zhao , Yitian Ding , Chen Jia , Yining Wang , Zifan Qian This is my paper
classification cs.CL
keywords genderbiaslanguagesacrosslanguagellmsbiasescontext
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    The paper presents GenderLexicon and a ClozeGender score, reporting that action verbs and object nouns carry gender bias beyond occupational stereotypes in English and Japanese language models.

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    A new India-focused benchmark shows that popular LLMs exhibit measurable negative bias against marginalized Indian identities and frequently reinforce caste, religion, region, and tribe stereotypes.

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  5. Gender Bias in English-to-Greek Machine Translation

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