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Title: Gender and Representation Bias in GPT-3 Generated Stories
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.  more » « less
Award ID(s):
1942591
PAR ID:
10237395
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the Third Workshop on Narrative Understanding
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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