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Large Language Models (LLMs) are capable of generating text that is similar to or surpasses human quality.
Www’18 open challenge: financial opinion mining and question answering
Maia, M., Handschuh, S., Freitas, A., Davis, B., McDermott, R., Zarrouk, M., and Balahur, A · 1942
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Writing through time: longitudinal studies of the effects of new technology on writing
Hartley, J., Howe, M., and McKeachie, W · 2001
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Using new technology to assess the academic writing styles of male and female pairs and individuals
Hartley, J., Pennebaker, J. W., and Fox, C · 2003
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Voice in academic writing: The rhetorical construction of author identity in blind manuscript review
Matsuda, P. K., and Tardy, C. M · 2007
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The linguistic features of writing quality
McNamara, D. S., Crossley, S. A., , and McCarthy, P. M · 2010
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Natural language processing: an introduction
Nadkarni, P. M., Ohno-Machado, L., and Chapman, W. W · 2011
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You’ve got style: Detecting writing flexibility across time
Snow, E. L., Allen, L. K., Jacovina, M. E., Perret, C. A., and McNamara, D. S · 2015
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Xgboost: A scalable tree boosting system
Chen, T., and Guestrin, C · 2016
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WikiQA: A challenge dataset for open-domain question answering
Yang, Y., Yih, W.-t., and Meek, C · 2018
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ELI5: Long form question answering
Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., and Auli, M · 2019
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A survey on stylometric text features
Lagutina, K., Lagutina, N., Boychuk, E., Vorontsova, I., Shliakhtina, E., Belyaeva, O., Paramonov, I., and Demidov, P. G · 2019
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Multiple-attribute text rewriting
Lample, G., Subramanian, S., Smith, E., Denoyer, L., Ranzato, M., and Boureau, Y · 2019
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MedDialog: Large-scale medical dialogue datasets
Zeng, G., Yang, W., Ju, Z., Yang, Y., Wang, S., Zhang, R., Zhou, M., Zeng, J., Dong, X., Zhang, R., Fang, H., Zhu, P., Chen, S., and Xie, P · 2020
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Cited alongside, same era.
Computer aided functional style identification and correction in modern Russian texts
Savchenko, E., and Lazebnik, T · 2022
Cited alongside, same era.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
Cited alongside, same era.
Conda: Contrastive domain adaptation for ai-generated text detection
Bhattacharjee, A., Kumarage, T., Moraffah, R., and Liu, H · 2023
Cited alongside, same era.
Can linguists distinguish between chatgpt/ai and human writing?: A study of research ethics and academic publishing
Casal, J. E., and Kessler, M · 2023
Cited alongside, same era.
Outfox: Llm-generated essay detection through in-context learning with adversarially generated examples
Koike, R., Kaneko, M., and Okazaki, N · 2023
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Chatgpt and bard exhibit spontaneous citation fabrication during psychiatry literature search
McGowan, A., Gui, Y., Dobbs, M., Shuster, S., Cotter, M., Selloni, A., Goodman, M., Srivastava, A., Cecchi, G. A., and Corcoran, C. M · 2023
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Contrasting linguistic patterns in human and llm-generated text
Muñoz-Ortiz, A., Gómez-Rodríguez, C., and Vilares, D · 2023
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Can ai-generated text be reliably detected?
Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., and Feizi, S · 2023
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Detectllm: Leveraging log rank information for zero-shot detection of machine-generated text
Su, J., Zhuo, T. Y., Wang, D., and Nakov, P · 2023
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Efficient detection of llm-generated texts with a bayesian surrogate model
Deng, Z., Gao, H., Miao, Y., and Zhang, H · 2023
Cited alongside, same era.
Distinguishing academic science writing from humans or chatgpt with over 99% accuracy using off-the-shelf machine learning tools
Desaire, H., Chua, A. E., Isom, M., Jarosova, R., and Hua, D · 2023
Cited alongside, same era.
A survey on the possibilities & impossibilities of ai-generated text detection
Ghosal, S. S., Chakraborty, S., Geiping, J., Huang, F., Manocha, D., and Bedi, A · 2023
Cited alongside, same era.
I slept like a baby: using human traits to characterize deceptive chatgpt and human text
Giorgi, S., Markowitz, D. M., Soni, N., Varadarajan, V., Mangalik, S., and Schwartz, H. A · 2023
Cited alongside, same era.
Llm censorship: A machine learning challenge or a computer security problem?
Glukhov, D., Shumailov, I., Gal, Y., Papernot, N., and Papyan, V · 2023
Cited alongside, same era.
How close is chatgpt to human experts? comparison corpus, evaluation, and detection
Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., and Wu, Y · 2023
Cited alongside, same era.
A large-scale comparison of human-written versus chatgpt-generated essays
Herbold, S., Hautli-Janisz, A., Heuer, U., Kikteva, Z., and Trautsch, A · 2023
Cited alongside, same era.
The science of detecting llm-generated texts
Tang, R., Chuang, Y.-N., and Hu, X · 2023
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Ghostbuster: Detecting text ghostwritten by large language models
Verma, V., Fleisig, E., Tomlin, N., and Klein, D · 2023
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M4: Multi-generator, multi-domain, and multi-lingual black-box machine-generated text detection
Wang, Y., Mansurov, J., Ivanov, P., Su, J., Shelmanov, A., Tsvigun, A., Whitehouse, C., Afzal, O. M., Mahmoud, T., Aji, A. F., et al · 2023
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Fake news in sheep’s clothing: Robust fake news detection against llm-empowered style attacks
Wu, J., and Hooi, B · 2023
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A survey on llm-gernerated text detection: Necessity, methods, and future directions
Wu, J., Yang, S., Zhan, R., Yuan, Y., Wong, D. F., and Chao, L. S · 2023
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A survey of large language models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al · 2023
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Authorship obfuscation in multilingual machine-generated text detection
Macko, D., Moro, R., Uchendu, A., Srba, I., Lucas, J. S., Yamashita, M., Tripto, N. I., Lee, D., Simko, J., and Bielikova, M · 2024
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