Cited by 293Ethics and Social Impacts of AITopic ModelingMedia, Gender, and Advertising

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Gender and Representation Bias in GPT-3 Generated Stories

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Lucy, Li, Bamman, David·2021·Proceedings of the Third Workshop on Narrative Understanding

DOI 10.18653/v1/2021.nuse-1.5

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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 1 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.

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Authors: Lucy, L., & Bamman, D.
Affiliations: University of California, Berkeley (US)
ORCID: Lucy, Li
Bamman, David
Venue: Proceedings of the Third Workshop on Narrative Understanding
Publisher: Association for Computational Linguistics
Published: 2021
Pages: 48-55
DOI: 10.18653/v1/2021.nuse-1.5
OpenAlex: W3168771811
Cited by: 287
References: 48
Citations by year: 2026: 40 · 2025: 63 · 2024: 65 · 2023: 97 · 2022: 18 · 2021: 4
FWCI: 41.66
Type: conference paper
Language: en
Open access: gold
doi.org
Licence: cc-by
Funders: National Science Foundation (1942591, 1752814)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Social Sciences › Gender Studies › Media, Gender, and Advertising
Keywords: gender stereotypes, representation bias, GPT-3, large language models, story generation, character gender

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Gender and Representation Bias in GPT-3 Generated Stories

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