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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
(2001) · Cited by 13,930
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 2×
“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
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887
Measuring praise and criticism
Turney, P. D., & Littman, M. L. (2003) · ACM Transactions on Information Systems · doi:10.1145/944012.944013 · Cited by 1,515
Harvesting implicit group attitudes and beliefs from a demonstration web site.
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
Kozlowski, A. C., Taddy, M., & Evans, J. A. (2019) · American Sociological Review · doi:10.1177/0003122419877135 · Cited by 741
Universal Adversarial Triggers for Attacking and Analyzing NLP
Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1221 · Cited by 642
The Woman Worked as a Babysitter: On Biases in Language Generation
Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1339 · Cited by 537 · cited 1×
“Language models generate different occupations and levels of respect for different genders, races, and sexual orientations (Sheng et al., 2019; Kirk et al., 2021).”
BackgroundPersistent Anti-Muslim Bias in Large Language Models
Abid, A., Farooqi, M., & Zou, J. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462624 · Cited by 476
Gender Bias in Coreference Resolution
Rudinger, R., Naradowsky, J., Leonard, B., & Van Durme, B. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2002 · Cited by 443
Counter-fitting Word Vectors to Linguistic Constraints
Mrkšić, N., Séaghdha, D. Ó., Thomson, B., Gašić, M., Rojas-Barahona, L. M., Su, P.-H., Vandyke, D., Wen, T.-H., & Young, S. J. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1018 · Cited by 425
Empath: Understanding Topic Signals in Large-Scale Text
Fast, E., Chen, B., & Bernstein, M. S. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858535 · Cited by 413
Boyd-Graber, J. L., Hu, Y., & Mimno, D. (2017) · now publishers, Inc. eBooks · doi:10.1561/1500000030 · Cited by 327
Creative Writing with a Machine in the Loop: Case Studies on Slogans and Stories
Clark, E., Ross, A. S., Tan, C., Ji, Y., & Smith, N. A. (2018) · 23rd International Conference on Intelligent User Interfaces · doi:10.1145/3172944.3172983 · Cited by 257
Can GPT-3 pass a writer’s turing test?
Elkins, K., & Chun, J. (2020) · Journal of Cultural Analytics · doi:10.22148/001c.17212 · Cited by 202
A Bayesian Mixed Effects Model of Literary Character
Bamman, D., Underwood, T., & Smith, N. A. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1035 · Cited by 191
Self-citationLearning Latent Personas of Film Characters
Bamman, D., O'Connor, B., & Smith, N. A. (2018) · Figshare · doi:10.1184/r1/6473477 · Cited by 179
Self-citationFlicker, E. (2003) · Public Understanding of Science · doi:10.1177/0963662503123009 · Cited by 167
Towards Controllable Biases in Language Generation
Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.291 · Cited by 123
The Transformation of Gender in English-Language Fiction
Underwood, T., Bamman, D., & Lee, S. (2018) · Journal of Cultural Analytics · doi:10.22148/16.019 · Cited by 121
Self-citationLong, M., Steinke, J., Applegate, B. E., Lapinski, M. K., Johnson, M. J., & Ghosh, S. (2010) · Science Communication · doi:10.1177/1075547009357779 · Cited by 119
Lucy, L., Demszky, D., Bromley, P., & Jurafsky, D. (2020) · AERA Open · doi:10.1177/2332858420940312 · Cited by 119
Self-citationThe Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us
Goldstone, A., & Underwood, T. (2014) · New Literary History · doi:10.1353/nlh.2014.0025 · Cited by 116
Gender as a Variable in Natural-Language Processing: Ethical Considerations
Larson, B. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing · doi:10.18653/v1/w17-1601 · Cited by 111
Jane, John … Leslie? A Historical Method for Algorithmic Gender Prediction
Blevins, C., & Mullen, L. A. (2015) · Digital humanities quarterly · doi:10.63744/2h8m9cv8t9y4 · Cited by 109
Singular they and the syntactic representation of gender in English
Bjorkman, B. M. (2017) · Glossa a journal of general linguistics · doi:10.5334/gjgl.374 · Cited by 106
STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation
Akoury, N., Wang, S., Whiting, J., Hood, S., Peng, N., & Iyyer, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.525 · Cited by 102
Connotation Frames of Power and Agency in Modern Films
Sap, M., Prasettio, M. C., Holtzman, A., Rashkin, H., & Choi, Y. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1247 · Cited by 97
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
An, J., Kwak, H., & Ahn, Y. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1228 · Cited by 71
Syntactic and cognitive issues in investigating gendered coreference
Ackerman, L. M. (2019) · Glossa a journal of general linguistics · doi:10.5334/gjgl.721 · Cited by 66
An Annotated Dataset of Coreference in English LiteraturePreprint
Bamman, D., Lewke, O., & Mansoor, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.01140 · Cited by 59
Self-citation“You are grounded!”: Latent Name Artifacts in Pre-trained Language Models
Shwartz, V., Rudinger, R., & Tafjord, O. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.556 · Cited by 52
Why Are We Like This?: The AI Architecture of a Co-Creative Storytelling Game
Kreminski, M., Dickinson, M., Mateas, M., & Wardrip-Fruin, N. (2020) · International Conference on the Foundations of Digital Games · doi:10.1145/3402942.3402953 · Cited by 50 · cited 1×
“Advances in large language models have allowed new possibilities for their use in storytelling, such as machine-in-the-loop creative writing (Clark et al., 2018; Kreminski et al., 2020; Akoury et al., 2020) and narrative generation for games (Raley and Hua, 2020).”
BackgroundFast, E., Vachovsky, T., & Bernstein, M. S. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14744 · Cited by 43
Social Characters: The Hierarchy of Gender in Contemporary English-Language Fiction
Kraicer, E., & Piper, A. (2019) · Journal of Cultural Analytics · doi:10.22148/16.032 · Cited by 40
Toward Gender-Inclusive Coreference Resolution
Cao, Y. T., & Daumé, H., III (2020) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/2020.acl-main.418 · Cited by 22
Playing With Unicorns: AI Dungeon and Citizen NLP
Hua, M. T., & Raley, R. (2020) · Digital humanities quarterly · doi:10.63744/8c9hrrx4banb · Cited by 20
Geek Chic: Smart Women in Popular Culture
Inness, S. A. (2007) · Palgrave Macmillan US eBooks · doi:10.1007/978-1-137-08421-7 · Cited by 19
Gender bias at scale: Evidence from the usage of personal names
Johns, B. T., & Dye, M. (2019) · Behavior Research Methods · doi:10.3758/s13428-019-01234-0 · Cited by 16
Analyzing Gender Bias within Narrative Tropes
Gala, D., Khursheed, M. O., Lerner, H., O’Connor, B., & Iyyer, M. (2020) · Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) · doi:10.18653/v1/2020.nlpcss-1.23 · Cited by 16
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling
Hoyle, A., Wolf-Sonkin, L., Wallach, H., Augenstein, I., & Cotterell, R. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1167 · Cited by 15
Geek chic: smart women in popular culture
(2008) · Choice Reviews Online · doi:10.5860/choice.45-2402 · Cited by 6
How True is GPT-2? An Empirical Analysis of Intersectional Occupational Biases.Preprint
Kirk, H. R., Jun, Y., Haider, I., Benussi, E., Volpin, F., Dreyer, F. A., Shtedritski, A., & Asano, Y. M. (2021) · arXiv (Cornell University) · Cited by 5
Research works printed but not linked: 1
- Stacy L Smith, Marc Choueiti, Ashley Prescott, and Katherine Pieper. 2012. Gender roles & occupa- tions: A look at character attributes and job-related aspirations in film and television. Geena Davis In- stitute on Gender in Media, pages 1-46.
Cited by
Sorted by citations
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
