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Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Improving language understanding by generative pre-training
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, et al · 2018
Earlier work this paper cites.
Gltr: Statistical detection and visualization of generated text
S. Gehrmann, H. Strobelt, and A. M. Rush · 2019
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The curious case of neural text degeneration
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi · 2019
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Automatic detection of generated text is easiest when humans are fooled
D. Ippolito, D. Duckworth, C. Callison-Burch, and D. Eck · 2019
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Release strategies and the social impacts of language models
I. Solaiman, M. Brundage, J. Clark, A. Askell, A. Herbert-Voss, J. Wu, A. Radford, G. Krueger, J. W. Kim, S. Kreps, et al · 2019
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Defending against neural fake news
R. Zellers, A. Holtzman, H. Rashkin, Y. Bisk, A. Farhadi, F. Roesner, and Y. Choi · 2019
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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The turking test: Can language models understand instructions?
A. Efrat and O. Levy · 2020
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Probabilistically masked language model capable of autoregressive generation in arbitrary word order
Y. Liao, X. Jiang, and Q. Liu · 2020
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Detecting cross-modal inconsistency to defend against neural fake news
R. Tan, B. A. Plummer, and K. Saenko · 2020
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All that’s’ human’is not gold: Evaluating human evaluation of generated text
E. Clark, T. August, S. Serrano, N. Haduong, S. Gururangan, and N. A. Smith · 2021
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Tweepfake: About detecting deepfake tweets
T. Fagni, F. Falchi, M. Gambini, A. Martella, and M. Tesconi · 2021
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Turingbench: A benchmark environment for turing test in the age of neural text generation
A. Uchendu, Z. Ma, T. Le, R. Zhang, and D. Lee · 2021
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Artificial hallucinations in chatgpt: implications in scientific writing
H. Alkaissi and S. I. McFarlane · 2023
Cited alongside, same era.
The confident wrongness of chatgpt, 2023
S. Allardice · 2023
Cited alongside, same era.
Y. Bang, S. Cahyawijaya, N. Lee, W. Dai, D. Su, B. Wilie, H. Lovenia, Z. Ji, T. Yu, W. Chung, et al · 2023
Cited alongside, same era.
N. Bian, X. Han, L. Sun, H. Lin, Y. Lu, and B. He · 2023
Cited alongside, same era.
Lawyer used chatgpt in court—and cited fake cases. a judge is considering sanctions, 2023
M. Bohanno · 2023
Cited alongside, same era.
A watermark for large language models
J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, and T. Goldstein · 2023
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Chatgpt: Jack of all trades, master of none
J. Kocoń, I. Cichecki, O. Kaszyca, M. Kochanek, D. Szydło, J. Baran, J. Bielaniewicz, M. Gruza, A. Janz, K. Kanclerz, et al · 2023
Closest in time.
Stylometric detection of ai-generated text in twitter timelines
T. Kumarage, J. Garland, A. Bhattacharjee, K. Trapeznikov, S. Ruston, and H. Liu · 2023
Closest in time.
Detectgpt: Zero-shot machine-generated text detection using probability curvature
E. Mitchell, Y. Lee, A. Khazatsky, C. D. Manning, and C. Finn · 2023
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Gpt-4 technical report
R. OpenAI · 2023
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S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al · 2023
Cited alongside, same era.
How is chatgpt’s behavior changing over time?
L. Chen, M. Zaharia, and J. Zou · 2023
Cited alongside, same era.
Undetectable watermarks for language models
M. Christ, S. Gunn, and O. Zamir · 2023
Cited alongside, same era.
Chatgpt outperforms crowd-workers for text-annotation tasks
F. Gilardi, M. Alizadeh, and M. Kubli · 2023
Cited alongside, same era.
How close is chatgpt to human experts? comparison corpus, evaluation, and detection
B. Guo, X. Zhang, Z. Wang, M. Jiang, J. Nie, Y. Ding, J. Yue, and Y. Wu · 2023
Cited alongside, same era.
Annollm: Making large language models to be better crowdsourced annotators
X. He, Z. Lin, Y. Gong, A. Jin, H. Zhang, C. Lin, J. Jiao, S. M. Yiu, N. Duan, W. Chen, et al · 2023
Cited alongside, same era.
Why detecting ai-generated text is so difficult (and what to do about it), 2023
M. Heikkilä · 2023
Cited alongside, same era.
Closest in time.
Is chatgpt a general-purpose natural language processing task solver?
C. Qin, A. Zhang, Z. Zhang, J. Chen, M. Yasunaga, and D. Yang · 2023
Closest in time.
Junk websites filled with ai-generated text are pulling in money from programmatic ads, 2023
T. Ryan-Mosley · 2023
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Can ai-generated text be reliably detected?
V. S. Sadasivan, A. Kumar, S. Balasubramanian, W. Wang, and S. Feizi · 2023
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Rise of the newsbots: Ai-generated news websites proliferating online, 2023
M. Sadeghi and L. Arvanitis · 2023
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P. Törnberg · 2023
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Llama: Open and efficient foundation language models
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al · 2023
Closest in time.
Provable robust watermarking for ai-generated text
X. Zhao, P. Ananth, L. Li, and Y.-X. Wang · 2023
Closest in time.
Protecting language generation models via invisible watermarking
X. Zhao, Y.-X. Wang, and L. Li · 2023
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