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        <title>Gender and Representation Bias in GPT-3 Generated Stories</title>
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    <abstract>Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes. Generated stories depict different topics and descriptions depending on GPT-3’s perceived gender of the character in a prompt, with feminine characters more likely to be associated with family and appearance, and described as less powerful than masculine characters, even when associated with high power verbs in a prompt. Our study raises questions on how one can avoid unintended social biases when using large language models for storytelling.</abstract>
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    <identifier type="doi">10.18653/v1/2021.nuse-1.5</identifier>
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%0 Conference Proceedings
%T Gender and Representation Bias in GPT-3 Generated Stories
%A Lucy, Li
%A Bamman, David
%Y Akoury, Nader
%Y Brahman, Faeze
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Martin, Lara J.
%S Proceedings of the Third Workshop on Narrative Understanding
%D 2021
%8 June
%I Association for Computational Linguistics
%C Virtual
%F lucy-bamman-2021-gender
%X Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes. Generated stories depict different topics and descriptions depending on GPT-3’s perceived gender of the character in a prompt, with feminine characters more likely to be associated with family and appearance, and described as less powerful than masculine characters, even when associated with high power verbs in a prompt. Our study raises questions on how one can avoid unintended social biases when using large language models for storytelling.
%R 10.18653/v1/2021.nuse-1.5
%U https://aclanthology.org/2021.nuse-1.5
%U https://doi.org/10.18653/v1/2021.nuse-1.5
%P 48-55

Markdown (Informal)

[Gender and Representation Bias in GPT-3 Generated Stories](https://aclanthology.org/2021.nuse-1.5) (Lucy & Bamman, NUSE-WNU 2021)

• Gender and Representation Bias in GPT-3 Generated Stories (Lucy & Bamman, NUSE-WNU 2021)

ACL

• Li Lucy and David Bamman. 2021. Gender and Representation Bias in GPT-3 Generated Stories. In Proceedings of the Third Workshop on Narrative Understanding, pages 48–55, Virtual. Association for Computational Linguistics.