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Large Language Models (LLMs) are revolutionizing several areas of Artificial Intelligence.
Megatron-LM: Training multi-billion parameter language models using model parallelism, 2019
M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper, and B. Catanzaro · 1909
Earlier work this paper cites.
Fine-tuning language models from human preferences, 2019
D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, and G. Irving · 1909
Earlier work this paper cites.
Computing machinery and intelligence
A. M. Turing · 1950
Earlier work this paper cites.
The disposition toward originality
F. Barron · 1955
Earlier work this paper cites.
Conflict, Arousal, and Curiosity
D. E. Berlyne · 1960
Earlier work this paper cites.
An analysis of creativity
M. Rhodes · 1961
Earlier work this paper cites.
The conditions of creativity
J. S. Bruner · 1962
Earlier work this paper cites.
The processes of creative thinking
A. Newell, J. C. Shaw, and H. A. Simon · 1962
Earlier work this paper cites.
Aesthetics and Psychobiology
D. E. Berlyne · 1971
Earlier work this paper cites.
Stimulating Creativity. Volume 1
M. I. Stein · 1974
Earlier work this paper cites.
TALE-SPIN, an interactive program that writes stories
J. R. Meehan · 1977
Earlier work this paper cites.
The Printing Press as an Agent of Change: Communications and Cultural Transformations in Early-Modern Europe
E. Eisenstein · 1979
Earlier work this paper cites.
The social psychology of creativity: A componential conceptualization
T. M. Amabile · 1983
Earlier work this paper cites.
Creating a story-telling universe
M. Lebowitz · 1983
Earlier work this paper cites.
The Policeman’s Beard Is Half Constructed
Racter · 1984
Earlier work this paper cites.
Intrinsic Motivation and Self-Determination in Human Behavior
E. L. Deci and R. M. Ryan · 1985
Earlier work this paper cites.
Society, culture, and person: A systems view of creativity
M. Csikszentmihalyi · 1988
Earlier work this paper cites.
The Creative Process: A Computer Model of Creativity and Storytelling
S. R. Turner · 1994
Earlier work this paper cites.
Creativity In Context
T. M. Amabile · 1996
Earlier work this paper cites.
The Conscious Mind: In Search of a Fundamental Theory
D. J. Chalmers · 1996
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Feist goes global: A comparative analysis of the notion of originality in copyright law
D. J. Gervais · 2002
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The Creative Mind: Myths and Mechanisms
M. A. Boden · 2003
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Creativity and imagination
B. Gaut · 2003
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Modeling forms of surprise in artificial agents: empirical and theoretical study of surprise functions
L. Macedo, R. Reisenzein, and A. Cardoso · 2004
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A preliminary framework for description, analysis and comparison of creative systems
G. A. Wiggins · 2006
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Some empirical criteria for attributing creativity to a computer program
G. Ritchie · 2007
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Computer models of creativity
M. A. Boden · 2009
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Evaluating machine creativity
A. K. Jordanous · 2009
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The philosophy of creativity
B. Gaut · 2010
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Evaluating creativity in humans, computers, and collectively intelligent systems
M. L. Maher · 2010
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Narrative planning: Balancing plot and character
M. O. Riedl and R. M. Young · 2010
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Computational creativity: The final frontier?
S. Colton and G. A. Wiggins · 2012
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Using genetic algorithms to create meaningful poetic text
R. Manurung, G. Ritchie, and H. Thompson · 2012
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Computational modelling of poetry generation
P. Gervás · 2013
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On the properties of neural machine translation: Encoder-decoder approaches
K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio · 2014
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
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GhostWriter: Using an LSTM for automatic rap lyric generation
P. Potash, A. Romanov, and A. Rumshisky · 2015
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Generating sentences from a continuous space
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio · 2016
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Deep reinforcement learning from human preferences
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei · 2017
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Deep creations: Intellectual property and the automata
J.-M. Deltorn · 2017
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Do androids dream of electric copyright? comparative analysis of originality in artificial intelligence generated works
A. Guadamuz · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 2017
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A hybrid convolutional variational autoencoder for text generation
S. Semeniuta, A. Severyn, and E. Barth · 2017
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Continual learning with deep generative replay
H. Shin, J. K. Lee, J. Kim, and J. Kim · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
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SeqGAN: Sequence generative adversarial nets with policy gradient
L. Yu, W. Zhang, J. Wang, and Y. Yu · 2017
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Adversarial feature matching for text generation
Y. Zhang, Z. Gan, K. Fan, Z. Chen, R. Henao, D. Shen, and L. Carin · 2017
Cited alongside, same era.
Event representations for automated story generation with deep neural nets
L. J. Martin, P. Ammanabrolu, W. Hancock, S. Singh, B. Harrison, and M. O. Riedl · 2018
Cited alongside, same era.
Artificial intelligence & copyright: Section 9(3) or authorship without an author
T. Bond and S. Blair · 2019
Cited alongside, same era.
Learning to surprise: A composer-audience architecture
R. C. Bunescu and O. O. Uduehi · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Cited alongside, same era.
Can a machine think (anything new)? automation beyond simulation
M. B. Fazi · 2019
Taxonomy of risks posed by language models
L. Weidinger, J. Uesato, M. Rauh, C. Griffin, P.-S. Huang, J. Mellor, A. Glaese, M. Cheng, B. Balle, A. Kasirzadeh, C. Biles, S. Brown, Z. Kenton, W. Hawkins, T. Stepleton, A. Birhane, L. A. Hendricks, L. Rimell, W. Isaac, J. Haas, S. Legassick, G. Irving, and I. Gabriel · 2022
Later among the works it cites.
Adapting transformer language models for application in computational creativity: Generating german theater plays with varied topics
L. Wertz and J. Kuhn · 2022
Later among the works it cites.
Pretrained language model in continual learning: A comparative study
T. Wu, M. Caccia, Z. Li, Y.-F. Li, G. Qi, and G. Haffari · 2022
Later among the works it cites.
Using cognitive psychology to understand GPT-3
M. Binz and E. Schulz · 2023
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Personhood and AI: Why large language models don’t understand us, 2023
J. Browning · 2023
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Cited alongside, same era.
AI, agency and responsibility: the VW fraud case and beyond
D. G. Johnson and M. Verdicchio · 2019
Cited alongside, same era.
The Artist in the Machine
A. I. Miller · 2019
Cited alongside, same era.
Neural Poetry: Learning to Generate Poems Using Syllables
A. Zugarini, S. Melacci, and M. Maggini · 2019
Cited alongside, same era.
Is transformative use eating the world?
C. D. Asay, A. Sloan, and D. Sobczak · 2020
Cited alongside, same era.
Climbing towards NLU: On meaning, form, and understanding in the age of data
E. M. Bender and A. Koller · 2020
Cited alongside, same era.
Artificial intelligence as producer and consumer of copyright works: Evaluating the consequences of algorithmic creativity
E. Bonadio and L. McDonagh · 2020
Cited alongside, same era.
S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al · 2023
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Consciousness in artificial intelligence: Insights from the science of consciousness, 2023
P. Butlin, R. Long, E. Elmoznino, Y. Bengio, J. Birch, A. Constant, G. Deane, S. M. Fleming, C. Frith, X. Ji, R. Kanai, C. Klein, G. Lindsay, M. Michel, L. Mudrik, M. A. K. Peters, E. Schwitzgebel, J. Simon, and R. VanRullen · 2023
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Quantifying memorization across neural language models
N. Carlini, D. Ippolito, M. Jagielski, K. Lee, F. Tramer, and C. Zhang · 2023
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Help me write a poem: Instruction tuning as a vehicle for collaborative poetry writing
T. Chakrabarty, V. Padmakumar, and H. He · 2023
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PaLM: Scaling language modeling with pathways
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, P. Schuh, K. Shi, S. Tsvyashchenko, J. Maynez, A. Rao, P. Barnes, Y. Tay, N. Shazeer, V. Prabhakaran, …, and N. Fiedel · 2023
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Anthropomorphization of AI: Opportunities and risks
A. Deshpande, T. Rajpurohit, K. Narasimhan, and A. Kalyan · 2023
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Friend or foe? Exploring the implications of large language models on the science system, 2023
B. Fecher, M. Hebing, M. Laufer, J. Pohle, and F. Sofsky · 2023
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How to cheat on your final paper: Assigning ai for student writing
P. Fyfe · 2023
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Gemini: A family of highly capable multimodal models, 2023
Gemini Team and Google · 2023
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The banality of ChatGPT, 2022
E. Hoel · 2023
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Speech and Language Processing
D. Jurafsky and J. H. Martin · 2023
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The unreasonable effectiveness of recurrent neural networks, 2015
A. Karpathy · 2023
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A watermark for large language models
J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, and T. Goldstein · 2023
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How much do language models copy from their training data? evaluating linguistic novelty in text generation using RAVEN
R. T. McCoy, P. Smolensky, T. Linzen, J. Gao, and A. Celikyilmaz · 2023
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Is AI art another industrial revolution in the making?
A. Newton and K. Dhole · 2023
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Leveraging human preferences to master poetry
R. Pardinas, G. Huang, D. Vazquez, and A. Piché · 2023
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Generative agents: Interactive simulacra of human behavior
J. S. Park, J. C. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein · 2023
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When does in-context learning fall short and why? a study on specification-heavy tasks, 2023
H. Peng, X. Wang, J. Chen, W. Li, Y. Qi, Z. Wang, Z. Wu, K. Zeng, B. Xu, L. Hou, and J. Li · 2023
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Generative AI, generating precariousness for workers?, 2023
A. Ponce Del Castillo · 2023
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Direct preference optimization: Your language model is secretly a reward model
R. Rafailov, A. Sharma, E. Mitchell, S. Ermon, C. D. Manning, and C. Finn · 2023
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Automated inauthenticity, 2023
M. Ressler · 2023
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Computational creativity as meta search, 2018
M. O. Riedl · 2023
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Turing-NLG: A 17-Billion-Parameter Language Model by Microsoft, 2020
C. Rosset · 2023
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Role play with large language models
M. Shanahan, K. McDonell, and L. Reynolds · 2023
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How photography pioneered a new understanding of art, 2022
E. Silva · 2023
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Llama 2: Open foundation and fine-tuned chat models, 2023
H. Touvron, L. Martin, K. R. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D. M. Bikel, L. Blecher, C. C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, …, and T. Scialom · 2023
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Ai-generated artworks are disappointing at auction, 2019
T. Waite · 2023
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A survey of large language models, 2023
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, Y. Du, C. Yang, Y. Chen, Z. Chen, J. Jiang, R. Ren, Y. Li, X. Tang, Z. Liu, P. Liu, J.-Y. Nie, and J.-R. Wen · 2023
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Quality-Diversity through AI feedback
H. Bradley, A. Dai, H. Teufel, J. Zhang, K. Oostermeijer, M. Bellagente, J. Clune, K. Stanley, G. Schott, and J. Lehman · 2024
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A survey on in-context learning, 2024
Q. Dong, L. Li, D. Dai, C. Zheng, J. Ma, R. Li, H. Xia, J. Xu, Z. Wu, B. Chang, X. Sun, L. Li, and Z. Sui · 2024
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The llama 3 herd of models, 2024
A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al-Dahle, A. Letman, A. Mathur, A. Schelten, A. Yang, A. Fan, A. Goyal, A. Hartshorn, A. Yang, A. Mitra, A. Sravankumar, A. Korenev, A. Hinsvark, A. Rao, A. Zhang, …, and Z. Zhao · 2024
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Large language model based multi-agents: A survey of progress and challenges, 2024
T. Guo, X. Chen, Y. Wang, R. Chang, S. Pei, N. V. Chawla, O. Wiest, and X. Zhang · 2024
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RULER: What’s the real context size of your long-context language models?, 2024
C.-P. Hsieh, S. Sun, S. Kriman, S. Acharya, D. Rekesh, F. Jia, Y. Zhang, and B. Ginsburg · 2024
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A. Q. Jiang, A. Sablayrolles, A. Roux, A. Mensch, B. Savary, C. Bamford, D. S. Chaplot, D. de las Casas, E. B. Hanna, F. Bressand, G. Lengyel, G. Bour, G. Lample, L. R. Lavaud, L. Saulnier, M.-A. Lachaux, P. Stock, S. Subramanian, S. Yang, S. Antoniak, …, and W. E. Sayed · 2024
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Understanding the effects of RLHF on LLM generalisation and diversity
R. Kirk, I. Mediratta, C. Nalmpantis, J. Luketina, E. Hambro, E. Grefenstette, and R. Raileanu · 2024
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Evaluating large language models in theory of mind tasks, 2024
M. Kosinski · 2024
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Language models, like humans, show content effects on reasoning tasks
A. K. Lampinen, I. Dasgupta, S. C. Y. Chan, H. R. Sheahan, A. Creswell, D. Kumaran, J. L. McClelland, and F. Hill · 2024
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Talkin’ ’bout AI generation: Copyright and the generative-AI supply chain, 2024
K. Lee, A. F. Cooper, and J. Grimmelmann · 2024
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Long-context LLMs struggle with long in-context learning, 2024
T. Li, G. Zhang, Q. D. Do, X. Yue, and W. Chen · 2024
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(Ir)rationality and cognitive biases in large language models
O. Macmillan-Scott and M. Musolesi · 2024
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Language models are unsupervised multitask learners, 2019
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2024
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M. Wu, Y. Yuan, G. Haffari, and L. Wang · 2024
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