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Large language models (LLMs) have recently been proposed as general-purpose agents for experimental design, with claims that they can perform in-context experimental design.
On a measure of the information provided by an experiment
D. V. Lindley. 1956 · 1956
Earlier work this paper cites.
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Regine S Bohacek, Colin McMartin, and Wayne C Guida. 1996 · 1996
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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Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, and Máté Lengyel. 2011 · 2011
Earlier work this paper cites.
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Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, and Kristin A. Persson. 2013 · 2013
Earlier work this paper cites.
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David L Mobley and J Peter Guthrie. 2014 · 2014
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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Earlier work this paper cites.
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Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal. 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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Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
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Julia Carnevale, Eric Shifrut, Nupura Kale, William A Nyberg, Franziska Blaeschke, Yan Yi Chen, Zhongmei Li, Sagar P Bapat, Morgan E Diolaiti, Patrick O’Leary, and 1 others. 2022 · 2022
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Later among the works it cites.
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