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The recent popularity of large language models (LLMs) has brought a significant impact to boundless fields, particularly through their open-ended ecosystem such as the APIs, open-sourced models, and plugins.
Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging
E. Brill · 1995
Earlier work this paper cites.
Wordnet: a lexical database for english
George A Miller · 1995
Earlier work this paper cites.
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Linyang Li, Ruotian Ma, Qipeng Guo, Xiangyang Xue, and Xipeng Qiu · 2004
Earlier work this paper cites.
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Linyang Li, Yunfan Shao, Demin Song, Xipeng Qiu, and Xuanjing Huang · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
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Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander Miller, Arthur Szlam, and Jason Weston · 2016
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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Alexander L Gaunt, Matthew A Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, and Sam Webster · 2017
Earlier work this paper cites.
Black-box generation of adversarial text sequences to evade deep learning classifiers
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Earlier work this paper cites.
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Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang · 2018
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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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models, 2018
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2018
Earlier work this paper cites.
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Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom · 2018
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant · 2021
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Yasumasa Onoe, Michael JQ Zhang, Eunsol Choi, and Greg Durrett · 2021
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Qiyuan Zhang, Lei Wang, Sicheng Yu, Shuohang Wang, Yang Wang, Jing Jiang, and Ee-Peng Lim · 2021
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Adversarial reprogramming of text classification neural networks, 2019
Paarth Neekhara, Shehzeen Hussain, Shlomo Dubnov, and Farinaz Koushanfar · 2019
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