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Large language models (LLMs) are increasingly deployed as the service backend for LLM-integrated applications such as code completion and AI-powered search.
Statistics in psychology and education
Henry Edward Garrett · 1947
Earlier work this paper cites.
Digital signatures: A tutorial survey
Selim G Aki · 1983
Earlier work this paper cites.
Random oracles are practical: A paradigm for designing efficient protocols
Mihir Bellare and Phillip Rogaway · 1993
Earlier work this paper cites.
Twenty years of attacks on the rsa cryptosystem
Dan Boneh et al · 1999
Earlier work this paper cites.
The random oracle methodology, revisited
Ran Canetti, Oded Goldreich, and Shai Halevi · 2004
Earlier work this paper cites.
US secure hash algorithms (SHA and HMAC-SHA)
D Eastlake 3rd and Tony Hansen · 2006
Earlier work this paper cites.
Graph-based statistical language model for code
Anh Tuan Nguyen and Tien N. Nguyen · 2015
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Targeted backdoor attacks on deep learning systems using data poisoning
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Certified defenses for data poisoning attacks
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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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Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
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Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith · 2020
Earlier work this paper cites.
Lessons from archives: Strategies for collecting sociocultural data in machine learning
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Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze · 2020
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Gender bias in neural natural language processing
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta · 2020
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The radicalization risks of gpt-3 and advanced neural language models
Kris McGuffie and Alex Newhouse · 2020
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
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Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
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