The DOI matches its Crossref record; 45 of the 48 references it cites were found in a registry or an index, and 1 are not checked yet.
Gender and Representation Bias in GPT-3 Generated Stories
Open accessAbstract
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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“GPT-3 has been used to generate articles (Transformer et al., 2022), stories (Lucy & Bamman, 2021), and other types of written content, with some users reporting that the generated text is difficult to distinguish from text written by humans (Elkins & Chun, 2020).”
From Chatting and cheating: Ensuring academic integrity in the era of ChatGPT (2024) · cites this paper as Lucy & Bamman, 2021
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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
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“Language models mimic patterns in their training data, parroting or even amplifying social biases (Bender et al., 2021).”
BackgroundScience faculty’s subtle gender biases favor male students
Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1211286109 · Cited by 3,150
Semantics derived automatically from language corpora contain human-like biases
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Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002) · Group Dynamics: Theory, Research, and Practice · doi:10.1037/1089-2699.6.1.101 · Cited by 1,321
The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings
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Universal Adversarial Triggers for Attacking and Analyzing NLP
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Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087
BAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 3,939
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004
“The ChatGPT has unprecedented ability to carry out remarkably complex tasks like writing an article (GTP-3 et al., 2021), story, poem, an essay (Lucy & Bamman 2021), ability to provide a summary or expansion of a text, adjusting texts to reflect a different perspective, and even write and debug…”
BackgroundChatting about ChatGPT: how may AI and GPT impact academia and libraries?
Lund, B. D., & Wang, T. (2023) · Library Hi Tech News · doi:10.1108/lhtn-01-2023-0009 · Cited by 1,355
Metadata
- Authors
- Lucy, L., & Bamman, D.
- Affiliations
- University of California, Berkeley (US)
- Venue
- Proceedings of the Third Workshop on Narrative Understanding
- Published
- 2021
- Pages
- 48-55
- 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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