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Recent improvements in the quality of the generations by large language models have spurred research into identifying machine-generated text.
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
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, et al. 2019 · 1908
Earlier work this paper cites.
A new readability yardstick
Rudolph Flesch. 1948 · 1948
Earlier work this paper cites.
spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 2017 · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee. 2017 · 2017
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.
Feature-based detection of automated language models: tackling gpt-2, gpt-3 and grover
Leon Fröhling and Arkaitz Zubiaga. 2021 · 2021
Earlier work this paper cites.
Automatic detection of entity-manipulated text using factual knowledge
Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks Lakshmanan. 2022 · 2022
Cited alongside, same era.
A benchmark dataset to distinguish human-written and machine-generated scientific papers
Mohamed Hesham Ibrahim Abdalla, Simon Malberg, Daryna Dementieva, Edoardo Mosca, and Georg Groh. 2023 · 2023
Cited alongside, same era.
Machine-generated text detection using deep learning
Raghav Gaggar, Ashish Bhagchandani, and Harsh Oza. 2023 · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Authentigpt: Detecting machine-generated text via black-box language models denoising
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.
The science of detecting llm-generated texts
Ruixiang Tang, Yu-Neng Chuang, and Xia Hu. 2023 · 2023
Later among the works it cites.
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.
Outfox: Llm-generated essay detection through in-context learning with adversarially generated examples
Ryuto Koike, Masahiro Kaneko, and Naoaki Okazaki. 2024 · 2024
Closest in time.
Detecting ai generated text based on nlp and machine learning approaches
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Zhen Guo and Shangdi Yu. 2023 · 2023
Cited alongside, same era.
Mgtbench: Benchmarking machine-generated text detection
Xinlei He, Xinyue Shen, Zeyuan Chen, Michael Backes, and Yang Zhang. 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. 2023 · 2023
Cited alongside, same era.
Nuzhat Prova. 2024 · 2024
Closest in time.
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, Toru Sasaki, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, and Preslav Nakov. 2024 · 2024
Closest in time.
Watermarks in the sand: Impossibility of strong watermarking for generative models
Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, and Boaz Barak. 2024 · 2024
Closest in time.