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The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of sectors, including media, cybersecurity, public discourse, and education.
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The NarrativeQA reading comprehension challenge
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WikiQA: A challenge dataset for open-domain question answering
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Openwebtext corpus
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Openwebtext corpus, 2019
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Mitigating gender bias in natural language processing: Literature review
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Automatic detection of generated text is easiest when humans are fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck. 2020 · 2020
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Automatic detection of machine generated text: A critical survey
Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks Lakshmanan, V.S. 2020 · 2020
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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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Authorship attribution for neural text generation
Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee. 2020 · 2020
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Adversarial watermarking transformer: Towards tracing text provenance with data hiding
Sahar Abdelnabi and Mario Fritz. 2021 · 2021
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Hans WA Hanley and Zakir Durumeric. 2023 · 2023
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Mgtbench: Benchmarking machine-generated text detection
Xinlei He, Xinyue Shen, Zeyuan Chen, Michael Backes, and Yang Zhang. 2023 · 2023
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Ai, write an essay for me: A large-scale comparison of human-written versus chatgpt-generated essays
Steffen Herbold, Annette Hautli-Janisz, Ute Heuer, Zlata Kikteva, and Alexander Trautsch. 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
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Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021
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Measuring coding challenge competence with apps
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, et al. 2021 · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
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MassiveSumm: a very large-scale, very multilingual, news summarisation dataset
Daniel Varab and Natalie Schluter. 2021 · 2021
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Machine generated text: A comprehensive survey of threat models and detection methods
Evan Crothers, Nathalie Japkowicz, and Herna Viktor. 2022 · 2022
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Liam Dugan, Daphne Ippolito, Arun Kirubarajan, Sherry Shi, and Chris Callison-Burch. 2022 · 2022
Cited alongside, same era.
The ethical need for watermarks in machine-generated language
Alexei Grinbaum and Laurynas Adomaitis. 2022 · 2022
Cited alongside, same era.
Xiaomeng Hu, Pin-Yu Chen, and Tsung-Yi Ho. 2023 · 2023
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Evading watermark based detection of ai-generated content
Zhengyuan Jiang, Jinghuai Zhang, and Neil Zhenqiang Gong. 2023 · 2023
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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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Language generation models can cause harm: So what can we do about it? an actionable survey
Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos, and Yulia Tsvetkov. 2023 · 2023
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How reliable are ai-generated-text detectors? an assessment framework using evasive soft prompts
Tharindu Kumarage, Paras Sheth, Raha Moraffah, Joshua Garland, and Huan Liu. 2023 · 2023
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Who wrote this code? watermarking for code generation
Taehyun Lee, Seokhee Hong, Jaewoo Ahn, Ilgee Hong, Hwaran Lee, Sangdoo Yun, Jamin Shin, and Gunhee Kim. 2023 · 2023
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Gpt detectors are biased against non-native english writers
Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu, and James Zou. 2023 · 2023
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Large language models can be guided to evade ai-generated text detection
Ning Lu, Shengcai Liu, Rui He, and Ke Tang. 2023 · 2023
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Multitude: Large-scale multilingual machine-generated text detection benchmark
Dominik Macko, Robert Moro, Adaku Uchendu, Jason Samuel Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, and Maria Bielikova. 2023 · 2023
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Smaller language models are better black-box machine-generated text detectors
Fatemehsadat Mireshghallah, Justus Mattern, Sicun Gao, Reza Shokri, and Taylor Berg-Kirkpatrick. 2023 · 2023
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Detectgpt: Zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D Manning, and Chelsea Finn. 2023 · 2023
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On the risk of misinformation pollution with large language models
Yikang Pan, Liangming Pan, Wenhu Chen, Preslav Nakov, Min-Yen Kan, and William Yang Wang. 2023 · 2023
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Game of tones: Faculty detection of gpt-4 generated content in university assessments
Mike Perkins, Jasper Roe, Darius Postma, James McGaughran, and Don Hickerson. 2023 · 2023
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Scaling speech technology to 1,000+ languages
Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, et al. 2023 · 2023
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On the zero-shot generalization of machine-generated text detectors
Xiao Pu, Jingyu Zhang, Xiaochuang Han, Yulia Tsvetkov, and Tianxing He. 2023 · 2023
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Is chatgpt a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 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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Red teaming language model detectors with language models
Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, and Cho-Jui Hsieh. 2023 · 2023
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Alejo Jose G Sison, Marco Tulio Daza, Roberto Gozalo-Brizuela, and Eduardo C Garrido-Merchán. 2023 · 2023
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The science of detecting llm-generated texts
Ruixiang Tang, Yu-Neng Chuang, and Xia Hu. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
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Gptzero: An ai text detector
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Multiscale positive-unlabeled detection of ai-generated texts
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Chatlog: Recording and analyzing chatgpt across time
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Intrinsic dimension estimation for robust detection of ai-generated texts
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Gpt-who: An information density-based machine-generated text detector
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Ai usage cards: Responsibly reporting ai-generated content
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Tree-ring watermarks: Fingerprints for diffusion images that are invisible and robust
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Towards an understanding and explanation for mixed-initiative artificial scientific text detection
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Remark-llm: A robust and efficient watermarking framework for generative large language models
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Promptbench: Towards evaluating the robustness of large language models on adversarial prompts
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Robust multi-bit natural language watermarking through invariant features
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