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ChatGPT has the ability to generate grammatically flawless and seemingly-human replies to different types of questions from various domains.
Rosenfeld, R.: A maximum entropy approach to adaptive statistical language modeling (1996)
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Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S., Amodei, D.: Deep reinforcement learning from human preferences. Advances in neural information processing systems 30 (2017)
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Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774. Curran Associates, Inc. (2017)
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)
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2019
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2019
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Geluykens, J., Mitrović, S., Ortega Vázquez, C.E., Laino, T., Vaucher, A., De Weerdt, J.: Neural machine translation for conditional generation of novel procedures. In: Proceedings of the 54th Hawaii International Conference on System Sciences. p. 1091 (2021)
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OpenAI: Chatgpt: Optimizing language models for dialogue (2022), https://openai.com/blog/chatgpt/
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Tian, E.: (Jan 2023), https://gptzero.me/
2023
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020)
2020
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