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It has been suggested that large language models such as GPT-4 have acquired some form of understanding beyond the correlations among the words in text including some understanding of mathematics as well.
Interpolation and extrapolation of stationary random sequences
Andrey Kolmogorov · 1941
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Extrapolation, interpolation, and smoothing of stationary time series: With engineering applications
Norbert Wiener · 1942
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Cybernetics or Control and Communication in the Animal and the Machine
Norbert Wiener · 1948
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On tables of random numbers
Andrei N Kolmogorov · 1963
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The anatomy of a large-scale hypertextual web search engine
Sergey Brin and Lawrence Page · 1998
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Kolmogorov’s structure functions and model selection
Nikolai K Vereshchagin and Paul MB Vitányi · 2004
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Google’s PageRank and beyond: The science of search engine rankings
Amy N Langville and Carl D Meyer · 2006
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Sledgehammer: Judgement day
Sascha Böhme and Tobias Nipkow · 2010
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Methods for evaluating information sources: An annotated catalogue
Birger Hjørland · 2012
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Three years of experience with Sledgehammer, a practical link between automatic and interactive theorem provers
Lawrence C Paulsson and Jasmin C Blanchette · 2012
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Mastering the game of Go without human knowledge
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An introduction to Kolmogorov complexity and its applications
Ming Li and Paul Vitányi · 2019
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Language models are few-shot learners
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Jeremy Avigad, Leonardo De Moura, and Soonho Kong · 2021
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Thor: Wielding hammers to integrate language models and automated theorem provers
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How chatbots extrapolate: From guided missiles to guided prompts
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GPT-4 technical report, 2023
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Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, and Ilya Sutskever · 2022
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Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2023
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