Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models

X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” Advances in neural information processing systems , vol. 28, 2015

2015

Earlier work this paper cites.

K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “Fake news detection on social media: A data mining perspective,” ACM SIGKDD explorations newsletter , vol. 19, no. 1, pp. 22–36, 2017

2017

Earlier work this paper cites.

N. Ruchansky, S. Seo, and Y. Liu, “Csi: A hybrid deep model for fake news detection,” in CIKM , 2017, pp. 797–806

2017

Earlier work this paper cites.

J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proceedings of the national academy of sciences , vol. 114, no. 13, pp. 3521–3526, 2017

2017

Earlier work this paper cites.

K. Langvardt, “Regulating online content moderation,” Geo. LJ , vol. 106, p. 1353, 2017

2017

Earlier work this paper cites.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018

Original

2018

Earlier work this paper cites.

S. Jhaver, S. Ghoshal, A. Bruckman, and E. Gilbert, “Online harassment and content moderation: The case of blocklists,” ACM Transactions on Computer-Human Interaction (TOCHI) , vol. 25, no. 2, pp. 1–33, 2018

2018

Earlier work this paper cites.

N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International conference on machine learning . PMLR, 2019, pp. 2790–2799

2019

Earlier work this paper cites.

K. Shu, L. Cui, S. Wang, D. Lee, and H. Liu, “defend: Explainable fake news detection,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , 2019, pp. 395–405

2019

Earlier work this paper cites.

K. Shu, S. Wang, D. Lee, and H. Liu, Disinformation, misinformation, and fake news in social media . Springer, 2020

2020

Earlier work this paper cites.

X. Zhou and R. Zafarani, “A survey of fake news: Fundamental theories, detection methods, and opportunities,” ACM Computing Surveys (CSUR) , vol. 53, no. 5, pp. 1–40, 2020

2020

Earlier work this paper cites.

X. Zhang and A. A. Ghorbani, “An overview of online fake news: Characterization, detection, and discussion,” Information Processing & Management , vol. 57, no. 2, p. 102025, 2020

2020

Earlier work this paper cites.

P. Przybyla, “Capturing the style of fake news,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 01, 2020, pp. 490–497

2020

Earlier work this paper cites.

C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of machine learning research , vol. 21, no. 140, pp. 1–67, 2020

2020

Earlier work this paper cites.

J. A. Nasir, O. S. Khan, and I. Varlamis, “Fake news detection: A hybrid cnn-rnn based deep learning approach,” IJIM Data Insights , vol. 1, no. 1, p. 100007, 2021

2021

Earlier work this paper cites.

R. K. Kaliyar, A. Goswami, and P. Narang, “Fakebert: Fake news detection in social media with a bert-based deep learning approach,” Multimedia tools and applications , vol. 80, no. 8, pp. 11 765–11 788, 2021

2021

Earlier work this paper cites.

E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell, “On the dangers of stochastic parrots: Can language models be too big?” in Proceedings of the 2021 ACM conference on fairness, accountability, and transparency , 2021, pp. 610–623

2021

Earlier work this paper cites.

B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” arXiv preprint arXiv:2104.08691 , 2021

Original

2021

Cited alongside, same era.

X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” arXiv preprint arXiv:2101.00190 , 2021

Original

2021

Cited alongside, same era.

E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv preprint arXiv:2106.09685 , 2021

Original

2021

Cited alongside, same era.

P. K. Verma, P. Agrawal, and R. Prodan, “Welfake dataset for fake news detection in text data,” Zenodo. Available online at: https://zenodo. org/records/4561253 (accessed September 3, 2023) , 2021

2021

Cited alongside, same era.

B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth, “Recent advances in natural language processing via large pre-trained language models: A survey,” ACM Computing Surveys , vol. 56, no. 2, pp. 1–40, 2023

2023

Later among the works it cites.

X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang, “Gpt understands, too,” AI Open , 2023

2023

Later among the works it cites.

R. Zhang, J. Han, C. Liu, P. Gao, A. Zhou, X. Hu, S. Yan, P. Lu, H. Li, and Y. Qiao, “Llama-adapter: Efficient fine-tuning of language models with zero-init attention,” arXiv preprint arXiv:2303.16199 , 2023

Original

2023

Later among the works it cites.

W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez et al. , “Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,” See https://vicuna. lmsys. org (accessed 14 April 2023) , vol. 2, no. 3, p. 6, 2023

2023

Later among the works it cites.