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While Large Language Models (LLMs) have demonstrated impressive performance across natural language generation tasks, their ability to generate truly creative content-characterized by novelty, diversity, surprise, and quality-remains limited.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and 1 others. 2020 · 1901
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 1904
Earlier work this paper cites.
The disposition toward originality
Frank Barron. 1955 · 1955
Earlier work this paper cites.
The Nature of Human Intelligence
J.P. Guilford. 1967 · 1967
Earlier work this paper cites.
The creative mind: Myths and mechanisms
Margaret A Boden. 2004 · 2004
Earlier work this paper cites.
Creativity evaluation through latent semantic analysis
Kevin Dunbar and Eve Forster. 2009 · 2009
Earlier work this paper cites.
Evaluating creativity in humans, computers, and collectively intelligent systems
Mary Lou Maher. 2010 · 2010
Earlier work this paper cites.
Subjective scoring of divergent thinking: Examining the reliability of unusual uses, instances, and consequences tasks
Paul J Silvia. 2011 · 2011
Earlier work this paper cites.
The standard definition of creativity
Mark A Runco and Garrett J Jaeger. 2012 · 2012
Earlier work this paper cites.
Taking the us patent office criteria seriously: A quantitative three-criterion creativity definition and its implications
Dean Keith Simonton. 2012 · 2012
Earlier work this paper cites.
Understanding and quantifying creativity in lexical composition
Polina Kuznetsova, Jianfu Chen, and Yejin Choi. 2013 · 2013
Earlier work this paper cites.
Automated scoring of originality using semantic representations
J Harbinson and Henk Haarman. 2014 · 2014
Earlier work this paper cites.
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Pythagoras Karampiperis, Antonis Koukourikos, and Evangelia Koliopoulou. 2014 · 2014
Earlier work this paper cites.
Stimulating creativity: Individual procedures
Morris I Stein. 2014 · 2014
Earlier work this paper cites.
Estimating creativity with a multiple-measurement approach within scientific and artistic domains
Sergio Agnoli, Giovanni E Corazza, and Mark A Runco. 2016 · 2016
Earlier work this paper cites.
Applicant extracurricular involvement predicts creativity better than traditional admissions factors
Katherine N Cotter, Jean E Pretz, and James C Kaufman. 2016 · 2016
Earlier work this paper cites.
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Earlier work this paper cites.
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Ulrich Schroeders, Oliver Wilhelm, and Gabriel Olaru. 2016 · 2016
Earlier work this paper cites.
Missing creativity: The effect of cognitive workload on rater (dis-) agreement in subjective divergent-thinking scores
Boris Forthmann, Heinz Holling, Nima Zandi, Anne Gerwig, Pınar Çelik, Martin Storme, and Todd Lubart. 2017 · 2017
Earlier work this paper cites.
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Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
Earlier work this paper cites.
Defining creativity: Don’t we also need to define what is not creative?
Dean Keith Simonton. 2018 · 2018
Earlier work this paper cites.
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Genevieve M Cseh and Karl K Jeffries. 2019 · 2019
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Michele Loi, Eleonora Viganò, and Lonneke van der Plas. 2020 · 2020
Earlier work this paper cites.
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Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
Earlier work this paper cites.
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Roger E Beaty and Dan R Johnson. 2021 · 2021
Earlier work this paper cites.
The neglect of idea diversity in creative idea generation and evaluation
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Earlier work this paper cites.
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Razvan C. Bunescu and Oseremen O. Uduehi. 2022 · 2022
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
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Earlier work this paper cites.
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Dan Richard Johnson, J. Kaufman, Brendan S. Baker, John D. Patterson, Baptiste Barbot, Adam E. Green, Janet G. van Hell, Evan S. Kennedy, Grace F Sullivan, Christa L. Taylor, Thomas Ward, and Roger E. Beaty. 2022 · 2022
Earlier work this paper cites.
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Earlier work this paper cites.
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Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and 1 others. 2022 · 2022
Earlier work this paper cites.
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Earlier work this paper cites.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, and 1 others. 2022 · 2022
Earlier work this paper cites.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. 2023 · 2023
Earlier work this paper cites.
John Joon Young Chung, Ece Kamar, and Saleema Amershi. 2023 · 2023
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Pier-Luc De Chantal and Peter Organisciak. 2023 · 2023
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Li Fan, Kaixiang Zhuang, Xueyang Wang, Jingyi Zhang, Cheng Liu, Jing Gu, and Jiang Qiu. 2023 · 2023
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Fabrício Góes, Piotr Sawicki, Marek Grzes, Marco Volpe, and Jacob Watson. 2023 · 2023
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Laura O’Mahony, Leo Grinsztajn, Hailey Schoelkopf, and Stella Biderman. 2024 · 2024
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