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We propose a new method, Adversarial In-Context Learning (adv-ICL), to optimize prompt for in-context learning (ICL) by employing one LLM as a generator, another as a discriminator, and a third as a prompt modifier.
Rouge: A package for automatic evaluation of summaries
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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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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
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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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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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Earlier work this paper cites.
Controllable invariance through adversarial feature learning
Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, and Graham Neubig · 2017
Earlier work this paper cites.
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Earlier work this paper cites.
Shashi Narayan, Shay B Cohen, and Mirella Lapata · 2018
Earlier work this paper cites.
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Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei · 2022
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Large language models are human-level prompt engineers
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Autodan: Automatic and interpretable adversarial attacks on large language models
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Universal and transferable adversarial attacks on aligned language models
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