Fetching the paper…
Reading the bibliography…
Many commercial and open-source models claim to detect machine-generated text with extremely high accuracy (99% or more).
NLTK: The natural language toolkit
Steven Bird and Edward Loper. 2004 · 2004
Earlier work this paper cites.
Practical solutions to the problem of diagonal dominance in kernel document clustering
Derek Greene and Pádraig Cunningham. 2006 · 2006
Earlier work this paper cites.
Unsupervised multilingual sentence boundary detection
Tibor Kiss and Jan Strunk. 2006 · 2006
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
Earlier work this paper cites.
New alignment methods for discriminative book summarization
David Bamman and Noah A. Smith. 2013 · 2013
Earlier work this paper cites.
Probabilistic model for code with decision trees
Veselin Raychev, Pavol Bielik, and Martin Vechev. 2016 · 2016
Earlier work this paper cites.
Scaling and effectiveness of email masquerade attacks: Exploiting natural language generation
Shahryar Baki, Rakesh Verma, Arjun Mukherjee, and Omprakash Gnawali. 2017 · 2017
Earlier work this paper cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Earlier work this paper cites.
One million posts: A data set of german online discussions
Dietmar Schabus, Marcin Skowron, and Martin Trapp. 2017 · 2017
Earlier work this paper cites.
TL;DR: Mining Reddit to learn automatic summarization
Michael Völske, Martin Potthast, Shahbaz Syed, and Benno Stein. 2017 · 2017
Earlier work this paper cites.
Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi. 2018 · 2018
Earlier work this paper cites.
Texygen: A benchmarking platform for text generation models
Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
GLTR: Statistical detection and visualization of generated text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander Rush. 2019 · 2019
Earlier work this paper cites.
Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019 · 2019
Earlier work this paper cites.
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 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
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, Miles McCain, Alex Newhouse, Jason Blazakis, Kris McGuffie, and Jasmine Wang. 2019 · 2019
Earlier work this paper cites.
Deepfake bot submissions to federal public comment websites cannot be distinguished from human submissions
Max Weiss. 2019 · 2019
Earlier work this paper cites.
Poems dataset (nlp)
Micael Arman. 2020 · 2020
Earlier work this paper cites.
How effectively can machines defend against machine-generated fake news? an empirical study
Meghana Moorthy Bhat and Srinivasan Parthasarathy. 2020 · 2020
Earlier work this paper cites.
RecipeNLG: A cooking recipes dataset for semi-structured text generation
Michał Bień, Michał Gilski, Martyna Maciejewska, Wojciech Taisner, Dawid Wisniewski, and Agnieszka Lawrynowicz. 2020 · 2020
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Earlier work this paper cites.
RoFT: A tool for evaluating human detection of machine-generated text
Liam Dugan, Daphne Ippolito, Arun Kirubarajan, and Chris Callison-Burch. 2020 · 2020
Earlier work this paper cites.
Automatic detection of generated text is easiest when humans are fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck. 2020 · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
The limitations of stylometry for detecting machine-generated fake news
Tal Schuster, Roei Schuster, Darsh J. Shah, and Regina Barzilay. 2020 · 2020
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Earlier work this paper cites.
Attacking neural text detectors
Max Wolff. 2020 · 2020
Earlier work this paper cites.
All that’s ‘human’ is not gold: Evaluating human evaluation of generated text
Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A. Smith. 2021 · 2021
Earlier work this paper cites.
Robustness analysis of grover for machine-generated news detection
Rinaldo Gagiano, Maria Myung-Hee Kim, Xiuzhen Zhang, and Jennifer Biggs. 2021 · 2021
Cited alongside, same era.
arxiv paper abstracts
Sayak Paul and Soumik Rakshit. 2021 · 2021
Cited alongside, same era.
Turingbench: A benchmark environment for turing test in the age of neural text generation
Adaku Uchendu, Zeyu Ma, Thai Le, Rui Zhang, and Dongwon Lee. 2021 · 2021
Cited alongside, same era.
Fine-grained czech news article dataset: An interdisciplinary approach to trustworthiness analysis
Matyáš Boháček, Michal Bravanský, Filip Trhlík, and Václav Moravec. 2022 · 2022
Cited alongside, same era.
A mutation-based text generation for adversarial machine learning applications
Jesus Guerrero, Gongbo Liang, and Izzat Alsmadi. 2022 · 2022
Exploring semantic perturbations on grover
Pranav Kulkarni, Ziqing Ji, Yan Xu, Marko Neskovic, and Kevin Nolan. 2023 · 2023
Later among the works it cites.
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
Later among the works it cites.
Contrastive decoding: Open-ended text generation as optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2023 · 2023
Later among the works it cites.
Large language models can be guided to evade ai-generated text detection
Ning Lu, Shengcai Liu, Rui He, Qi Wang, Yew-Soon Ong, and Ke Tang. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Truncation sampling as language model desmoothing
John Hewitt, Christopher Manning, and Percy Liang. 2022 · 2022
Cited alongside, same era.
The stack: 3 tb of permissively licensed source code
Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, and Harm de Vries. 2022 · 2022
Cited alongside, same era.
Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini. 2022 · 2022
Cited alongside, same era.
ChatGPT: Optimizing Language Models for Dialogue
OpenAI. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Cited alongside, same era.
Threat scenarios and best practices to detect neural fake news
Artidoro Pagnoni, Martin Graciarena, and Yulia Tsvetkov. 2022 · 2022
Cited alongside, same era.
Cross-domain detection of GPT-2-generated technical text
Juan Diego Rodriguez, Todd Hay, David Gros, Zain Shamsi, and Ravi Srinivasan. 2022 · 2022
Cited alongside, same era.
Brady D Lund, Ting Wang, Nishith Reddy Mannuru, Bing Nie, Somipam Shimray, and Ziang Wang. 2023 · 2023
Later among the works it cites.
MULTITuDE: Large-scale multilingual machine-generated text detection benchmark
Dominik Macko, Robert Moro, Adaku Uchendu, Jason Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, and Maria Bielikova. 2023 · 2023
Later among the works it cites.
Locally typical sampling
Clara Meister, Tiago Pimentel, Gian Wiher, and Ryan Cotterell. 2023 · 2023
Later among the works it cites.
Detectgpt: zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, and Chelsea Finn. 2023 · 2023
Later among the works it cites.
Introducing mpt-30b: Raising the bar for open-source foundation models
NLP Team MosaicML. 2023 · 2023
Later among the works it cites.
Distinguishing fact from fiction: A benchmark dataset for identifying machine-generated scientific papers in the LLM era
Edoardo Mosca, Mohamed Hesham Ibrahim Abdalla, Paolo Basso, Margherita Musumeci, and Georg Groh. 2023 · 2023
Later among the works it cites.
OpenAI. 2023 · 2023
Later among the works it cites.
Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay. 2023 · 2023
Later among the works it cites.
Deepfake text detection: Limitations and opportunities
J. Pu, Z. Sarwar, S. Abdullah, A. Rehman, Y. Kim, P. Bhattacharya, M. Javed, and B. Viswanath. 2023a · 2023
Later among the works it cites.
Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2023 · 2023
Later among the works it cites.
Supervised machine-generated text detectors: Family and scale matters
Areg Mikael Sarvazyan, José Ángel González, Paolo Rosso, and Marc Franco-Salvador. 2023a · 2023
Later among the works it cites.
Areg Mikael Sarvazyan, José Ángel González, Marc Franco-Salvador, Francisco Rangel, Berta Chulvi, and Paolo Rosso. 2023b · 2023
Later among the works it cites.
Talking abortion (mis) information with chatgpt on tiktok
Filipo Sharevski, Jennifer Vander Loop, Peter Jachim, Amy Devine, and Emma Pieroni. 2023 · 2023
Later among the works it cites.
Ai model gpt-3 (dis)informs us better than humans
Giovanni Spitale, Nikola Biller-Andorno, and Federico Germani. 2023 · 2023
Later among the works it cites.
Gptzero: Towards detection of ai-generated text using zero-shot and supervised methods
Edward Tian and Alexander Cui. 2023 · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
Later among the works it cites.
Ghostbuster: Detecting text ghostwritten by large language models
Vivek Verma, Eve Fleisig, Nicholas Tomlin, and Dan Klein. 2023 · 2023
Later among the works it cites.
LLMDet: A third party large language models generated text detection tool
Kangxi Wu, Liang Pang, Huawei Shen, Xueqi Cheng, and Tat-Seng Chua. 2023 · 2023
Later among the works it cites.
On the generalization of training-based chatgpt detection methods
Han Xu, Jie Ren, Pengfei He, Shenglai Zeng, Yingqian Cui, Amy Liu, Hui Liu, and Jiliang Tang. 2023 · 2023
Later among the works it cites.
World-class ai, at your command
NLP Team Cohere. 2024 · 2024
Closest in time.
Llm-as-a-coauthor: The challenges of detecting llm-human mixcase
Chujie Gao, Dongping Chen, Qihui Zhang, Yue Huang, Yao Wan, and Lichao Sun. 2024 · 2024
Closest in time.
Spotting llms with binoculars: Zero-shot detection of machine-generated text
Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2024 · 2024
Closest in time.
Mage: Machine-generated text detection in the wild
Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, and Yue Zhang. 2024 · 2024
Closest in time.
Authorship obfuscation in multilingual machine-generated text detection
Dominik Macko, Robert Moro, Adaku Uchendu, Ivan Srba, Jason Samuel Lucas, Michiharu Yamashita, Nafis Irtiza Tripto, Dongwon Lee, Jakub Simko, and Maria Bielikova. 2024 · 2024
Closest in time.
Few-shot detection of machine-generated text using style representations
Rafael Rivera Soto, Kailin Koch, Aleem Khan, Barry Chen, Marcus Bishop, and Nicholas Andrews. 2024 · 2024
Closest in time.
Hc3 plus: A semantic-invariant human chatgpt comparison corpus
Zhenpeng Su, Xing Wu, Wei Zhou, Guangyuan Ma, and Songlin Hu. 2024 · 2024
Closest in time.