Fetching the paper…
Reading the bibliography…
Large language models possess remarkable capacity for processing language, but it remains unclear whether these models can further generate creative content.
Some new looks at the nature of creative processes
J. Pv Guilford. 1964 · 1964
Earlier work this paper cites.
A learning algorithm for boltzmann machines
David H. Ackley, Geoffrey E. Hinton, and Terrence J. Sejnowski. 1985 · 1985
Earlier work this paper cites.
WordNet: a lexical database for English
George A. Miller. 1995 · 1995
Earlier work this paper cites.
Evolutionary approaches to creativity
Liane Gabora and Scott Barry Kaufman. 2010 · 2010
Earlier work this paper cites.
The Standard Definition of Creativity
Mark A. Runco and Garrett J. Jaeger. 2012 · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Creative thinking as orchestrated by semantic processing vs. cognitive control brain networks
Anna Abraham. 2014 · 2014
Earlier work this paper cites.
The roles of associative and executive processes in creative cognition
Roger E. Beaty, Paul J. Silvia, Emily C. Nusbaum, Emanuel Jauk, and Mathias Benedek. 2014 · 2014
Earlier work this paper cites.
Glove: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Evaluation methods for unsupervised word embeddings
Tobias Schnabel, Igor Labutov, David Mimno, and Thorsten Joachims. 2015 · 2015
Earlier work this paper cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016 · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. 2016 · 2016
Earlier work this paper cites.
Hierarchical Neural Story Generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
Automating creativity assessment with semdis: An open platform for computing semantic distance
Roger E. Beaty and Dan Richard Johnson. 2020 · 2020
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
Word meaning in minds and machines
Brenden M. Lake and Gregory L. Murphy. 2020 · 2020
Cited alongside, same era.
Automating creativity assessment with SemDis: An open platform for computing semantic distance
Roger E. Beaty and Dan R. Johnson. 2021 · 2021
Cited alongside, same era.
Forward flow and creative thought: Assessing associative cognition and its role in divergent thinking
Roger E. Beaty, Daniel C. Zeitlen, Brendan S. Baker, and Yoed N. Kenett. 2021 · 2021
Cited alongside, same era.
Decoding Methods for Neural Narrative Generation
Alexandra DeLucia, Aaron Mueller, Xiang Lisa Li, and João Sedoc. 2021 · 2021
Cited alongside, same era.
Intelligence and creativity share a common cognitive and neural basis
Emily Frith, Daniel B. Elbich, Alexander P. Christensen, Monica D. Rosenberg, Qunlin Chen, Michael J. Kane, Paul J. Silvia, Paul Seli, and Roger E. Beaty. 2021 · 2021
Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023
Closest in time.
In BLOOM: Creativity and Affinity in Artificial Lyrics and Art
Evan Crothers, Herna L. Viktor, and Nathalie Japkowicz. 2023 · 2023
Closest in time.
Overlap in meaning is a stronger predictor of semantic activation in GPT-3 than in humans
Jan Digutsch and Michal Kosinski. 2023 · 2023
Closest in time.
Artificial muses: Generative artificial intelligence chatbots have risen to human-level creativity
Jennifer Haase and Paul H. P. Hanel. 2023 · 2023
Closest in time.
Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest
Jack Hessel, Ana Marasovic, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, and Yejin Choi. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Naming unrelated words predicts creativity
Jay A. Olson, Johnny Nahas, Denis Chmoulevitch, Simon J. Cropper, and Margaret E. Webb. 2021 · 2021
Cited alongside, same era.
Trading Off Diversity and Quality in Natural Language Generation
Hugh Zhang, Daniel Duckworth, Daphne Ippolito, and Arvind Neelakantan. 2021 · 2021
Cited alongside, same era.
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2022 · 2022
Cited alongside, same era.
On the probability–quality paradox in language generation
Clara Meister, Gian Wiher, Tiago Pimentel, and Ryan Cotterell. 2022 · 2022
Cited alongside, same era.
Introducing ChatGPT
OpenAI. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
Cited alongside, same era.
Chatgpt is fun, but it is not funny! humor is still challenging large language models
Sophie Jentzsch and Kristian Kersting. 2023 · 2023
Closest in time.
Challenges and Applications of Large Language Models
Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy. 2023 · 2023
Closest in time.
OpenAssistant Conversations – Democratizing Large Language Model Alignment
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi-Rui Tam, Keith Stevens, Abdullah Barhoum, Nguyen Minh Duc, Oliver Stanley, Richárd Nagyfi, Shahul ES, Sameer Suri, David Glushkov, Arnav Dantuluri, Andrew Maguire, Christoph Schuhmann, Huu Nguyen, and Alexander Mattick. 2023 · 2023
Closest in time.
Boosting Theory-of-Mind Performance in Large Language Models via Prompting
Shima Rahimi Moghaddam and Christopher J. Honey. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Can chatgpt be used to generate scientific hypotheses?
Yang Jeong Park, Daniel Kaplan, Zhichu Ren, Chia-Wei Hsu, Changhao Li, Haowei Xu, Sipei Li, and Ju Li. 2023 · 2023
Closest in time.
The curse of recursion: Training on generated data makes models forget
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson. 2023 · 2023
Closest in time.
Brainstorm, then Select: A Generative Language Model Improves Its Creativity Score
Douglas Summers-Stay, Clare R. Voss, and Stephanie M. Lukin. 2023 · 2023
Closest in time.
LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
Closest in time.
Retrieval flexibility links to creativity: evidence from computational linguistic measure
Jingyi Zhang, Kaixiang Zhuang, Jiangzhou Sun, Cheng Liu, Li Fan, Xueyang Wang, Jing Gu, and Jiang Qiu. 2023 · 2023
Closest in time.