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The widespread use of large language models (LLMs) is increasing the demand for methods that detect machine-generated text to prevent misuse.
Text processing like humans do: Visually attacking and shielding nlp systems
Steffen Eger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, and Iryna Gurevych. 2019 · 1903
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 1904
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Release strategies and the social impacts of language models
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, Sarah Kreps, et al. 2019 · 1908
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Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019 · 1908
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Binary codes capable of correcting deletions, insertions, and reversals
Vladimir I. Levenshtein. 1965 · 1965
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Advances in record-linkage methodology as applied to matching the 1985 census of tampa, florida
Matthew A. Jaro. 1989 · 1985
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Techniques for automatically correcting words in text
Karen Kukich. 1992 · 1992
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Use of a genetic algorithm in the crypt-analysis of simple substitution ciphers
Richard J. Spillman, Mark Janssen, Bob Nelson, and Martin Kepner. 1993 · 1993
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WordNet: A lexical database for English
George A. Miller. 1994 · 1994
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Letter frequency (english)
Mička Pavel. 2000 · 2000
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Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training
Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, and Ming Zhou. 2020 · 2001
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The homograph attack
Evgeniy Gabrilovich and Alex Gontmakher. 2002 · 2002
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Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 2020 · 2003
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How are spelling errors generated and corrected? a study of corrected and uncorrected spelling errors using keystroke logs
Yukino Baba and Hisami Suzuki. 2012 · 2012
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
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The effects of emoji in sentiment analysis
Serkan Ayvaz and Mohammed O. Shiha. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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Textbugger: Generating adversarial text against real-world applications
Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang. 2018 · 2018
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Gltr: Statistical detection and visualization of generated text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M Rush. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Adversarial attack on sentiment classification
Yi-Ting Tsai, Min-Chu Yang, and Han-Yu Chen. 2019 · 2019
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Language-agnostic visual-semantic embeddings
Jonatas Wehrmann, Mauricio A. Lopes, Douglas M. Souza, and Rodrigo C. Barros. 2019 · 2019
Cited alongside, same era.
Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Language gans falling short
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, and Laurent Charlin. 2020 · 2020
Cited alongside, same era.
Is bert really robust? a strong baseline for natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
Cited alongside, same era.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
GCPG: A general framework for controllable paraphrase generation
Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Haibo Zhang, Xue Zhao, Wenqing Yao, and Boxing Chen. 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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Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, Linyi Yang, and Yue Zhang. 2023 · 2023
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Can large language models be an alternative to human evaluations?
Cheng-Han Chiang and Hung-yi Lee. 2023 · 2023
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A systematic characterization of sampling algorithms for open-ended language generation
Moin Nadeem, Tianxing He, Kyunghyun Cho, and James Glass. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
Cited alongside, same era.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
Cited alongside, same era.
Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021 · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Cited alongside, same era.
Miranda Christ, Sam Gunn, and Or Zamir. 2023 · 2023
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Real or fake text?: Investigating human ability to detect boundaries between human-written and machine-generated text
Liam Dugan, Daphne Ippolito, Arun Kirubarajan, Sherry Shi, and Chris Callison-Burch. 2023 · 2023
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How close is chatgpt to human experts? comparison corpus, evaluation, and detection
Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023 · 2023
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On the blind spots of model-based evaluation metrics for text generation
Tianxing He, Jingyu Zhang, Tianle Wang, Sachin Kumar, Kyunghyun Cho, James Glass, and Yulia Tsvetkov. 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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Radar: Robust ai-text detection via adversarial learning
Xiaomeng Hu, Pin-Yu Chen, and Tsung-Yi Ho. 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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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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OpenAI. 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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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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string2string: A modern python library for string-to-string algorithms
Mirac Suzgun, Stuart M. Shieber, and Dan Jurafsky. 2023 · 2023
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M4: Multi-generator, multi-domain, and multi-lingual black-box machine-generated text detection
Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Alham Fikri Aji, et al. 2023 · 2023
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Why johnny can’t prompt: How non-ai experts try (and fail) to design llm prompts
J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, and Qiang Yang. 2023 · 2023
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Assaying on the robustness of zero-shot machine-generated text detectors
Yi-Fan Zhang, Zhang Zhang, Liang Wang, and Rong Jin. 2023 · 2023
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Shangbin Feng, Herun Wan, Ningnan Wang, Zhaoxuan Tan, Minnan Luo, and Yulia Tsvetkov. 2024 · 2024
Closest in time.
Does {DetectGPT} fully utilize perturbation? selective perturbation on model-based contrastive learning detector would be better
Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, and Chao Shen. 2024 · 2024
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Raidar: generative ai detection via rewriting
Chengzhi Mao, Carl Vondrick, Hao Wang, and Junfeng Yang. 2024 · 2024
Closest in time.