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Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection.
Least squares quantization in pcm
Seth Lloyd. 1982 · 1982
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Approximate nearest neighbors: Towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani. 1998 · 1998
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Generating informative and diverse conversational responses via adversarial information maximization
Yizhe Zhang, Michel Galley, Jianfeng Gao, Zhe Gan, Xiujun Li, Chris Brockett, and William B. Dolan. 2018 · 2018
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
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Booksum: A collection of datasets for long-form narrative summarization
Wojciech Kryściński, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong, and Dragomir Radev. 2021 · 2021
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Measuring and improving semantic diversity of dialogue generation
Seungju Han, Beomsu Kim, and Buru Chang. 2022 · 2022
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ChatGPT
OpenAI. 2022 · 2022
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Paraphrastic representations at scale
John Wieting, Kevin Gimpel, Graham Neubig, and Taylor Berg-kirkpatrick. 2022 · 2022
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OPT: Open Pre-trained Transformer Language Models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2022
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Provable robust watermarking for ai-generated text
Xuandong Zhao, Prabhanjan Ananth, Lei Li, and Yu-Xiang Wang. 2023 · 2022
Cited alongside, same era.
Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, and Mohit Iyyer. 2023 · 2023
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Robust distortion-free watermarks for language models
Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, and Percy Liang. 2023 · 2023
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A semantic invariant robust watermark for large language models
Aiwei Liu, Leyi Pan, Xuming Hu, Shiao Meng, and Lijie Wen. 2023 · 2023
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Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2023 · 2023
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Miranda Christ, Sam Gunn, and Or Zamir. 2023 · 2023
Cited alongside, same era.
Yu Fu, Deyi Xiong, and Yue Dong. 2023 · 2023
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Semstamp: A semantic watermark with paraphrastic robustness for text generation
Abe Bohan Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, and Yulia Tsvetkov. 2023 · 2023
Cited alongside, same era.
A watermark for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein. 2023a
Cited in the paper.
On the reliability of watermarks for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, and Tom Goldstein. 2023b
Cited in the paper.
Lean Wang, Wenkai Yang, Deli Chen, Haozhe Zhou, Yankai Lin, Fandong Meng, Jie Zhou, and Xu Sun. 2023 · 2023
Later among the works it cites.
Robust multi-bit natural language watermarking through invariant features
Kiyoon Yoo, Wonhyuk Ahn, Jiho Jang, and No Jun Kwak. 2023 · 2023
Later among the works it cites.
Stumbling blocks: Stress testing the robustness of machine-generated text detectors under attacks
Yichen Wang, Shangbin Feng, Abe Bohan Hou, Xiao Pu, Chao Shen, Xiaoming Liu, Yulia Tsvetkov, and Tianxing He. 2024 · 2024
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