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Rapid development of artificial intelligence has drastically accelerated the development of scientific discovery.
Étude comparative de la distribution florale dans une portion des alpes et des jura
Paul Jaccard · 1901
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The Logic of Scientific Discovery
Karl R. Popper · 1935
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NLTK: The natural language toolkit
Steven Bird and Edward Loper · 2004
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Language models are few-shot learners
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When and how to develop domain-specific languages
Marjan Mernik, Jan Heering, and Anthony M. Sloane · 2005
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The hutter prize, 2006
Marcus Hutter · 2006
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Examining the challenges of scientific workflows
Yolanda Gil, Ewa Deelman, Mark Ellisman, Thomas Fahringer, Geoffrey Fox, Dennis Gannon, Carole Goble, Miron Livny, Luc Moreau, and Jim Myers · 2007
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About the test data, 2011
Matt Mahoney · 2011
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The unreasonable effectiveness of recurrent neural networks, 2015
Andrej Karpathy · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Deep neural networks to enable real-time multimessenger astrophysics
Daniel George and EA Huerta · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W. Battaglia · 2020
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Learning to summarize from human feedback
Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano · 2020
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Accelerating materials discovery using machine learning
Yongfei Juan, Yongbing Dai, Yang Yang, and Jiao Zhang · 2021
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Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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DRIFT: A toolkit for diachronic analysis of scientific literature
Abheesht Sharma, Gunjan Chhablani, Harshit Pandey, and Rajaswa Patil · 2021
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Magnetic control of tokamak plasmas through deep reinforcement learning
Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de Las Casas, et al · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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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
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Social simulacra: Creating populated prototypes for social computing systems
Joon Sung Park, Lindsay Popowski, Carrie Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Towards possibilities & impossibilities of AI-generated text detection: A survey
Soumya Suvra Ghosal, Souradip Chakraborty, Jonas Geiping, Furong Huang, Dinesh Manocha, and Amrit Singh Bedi · 2023
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Ideas are dimes a dozen: Large language models for idea generation in innovation
Karan Girotra, Lennart Meincke, Christian Terwiesch, and Karl T Ulrich · 2023
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War and Peace (WarAgent): Large language model-based multi-agent simulation of world wars
Wenyue Hua, Lizhou Fan, Lingyao Li, Kai Mei, Jianchao Ji, Yingqiang Ge, Libby Hemphill, and Yongfeng Zhang · 2023
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The role of ChatGPT in scientific communication: writing better scientific review articles
Jingshan Huang and Ming Tan · 2023
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A watermark for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein · 2023
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Yuliang Liu, Xiangru Tang, Zefan Cai, Junjie Lu, Yichi Zhang, Yanjun Shao, Zexuan Deng, Helan Hu, Zengxian Yang, Kaikai An, et al · 2023
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ART: Automatic multi-step reasoning and tool-use for large language models
Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro · 2023
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Augmenting large language models with chemistry tools
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LLM and simulation as bilevel optimizers: A new paradigm to advance physical scientific discovery
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Exploring large language models for communication games: An empirical study on werewolf
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Protecting language generation models via invisible watermarking
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Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
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Accurate structure prediction of biomolecular interactions with AlphaFold 3
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Artificial Intelligence, Scientific Discovery, and Product Innovation
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Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformers
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Automated social science: Language models as scientist and subjects
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LLM critics help catch LLM bugs
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Mathematical discoveries from program search with large language models
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