Chatting about ChatGPT: How may AI and GPT impact academia and libraries?
Keywords:
ChatGPT, GPT-3, Generative Pre-Trained Transformer, AI, Academia, LibrariesAbstract
This paper provides an overview of key definitions related to ChatGPT, a public tool developed
by OpenAI, and its underlying technology, GPT. The paper discusses the history and technology
of GPT, including its generative pre-trained transformer model, its ability to perform a wide
range of language-based tasks, and how ChatGPT utilizes this technology to function as a
sophisticated chatbot. Additionally, the paper includes an interview with ChatGPT on its
potential impact on academia and libraries. The interview discusses the benefits of ChatGPT
such as improving search and discovery, reference and information services, cataloging and
metadata generation, and content creation, as well as the ethical considerations that need to be
taken into account, such as privacy and bias. The paper also explores the possibility of using
ChatGPT for writing scholarly papers
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References
Bishop, C. M. (1994). Neural networks and their applications. Review of Scientific Instruments,
, article 1803. https://doi.org/10.1063/1.1144830
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W.
(2016). Openai gym. arXiv. https://doi.org/10.48550/arXiv.1606.01540
Budzianowski, P., & Vulić, I. (2019). Hello, it's GPT-2--how can I help you? towards the use of
pretrained language models for task-oriented dialogue systems. arXiv.
https://doi.org/10.48550/arXiv.1907.05774
Cherian, A., Peng, K. C., Lohit, S., Smith, K., & Tenenbaum, J. B. (2022). Are Deep Neural
Networks SMARTer than Second Graders?. arXiv. https://doi.org/10.48550/arXiv.2212.09993
Dale, R. (2017). NLP in a post-truth world. Natural Language Engineering, 23(2), 319-324.
Dale, R. (2021). GPT-3 What’s it good for? Natural Language Engineering, 27(1), 113-118.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep
bidirectional transformers for language understanding. arXiv.
https://doi.org/10.48550/arXiv.1810.04805
Erhan, D., Bengio, Y., Courville, A., Manzagol, P., & Vincent, P. (2010). Why does
unsupervised pre-training help deep learning. Journal of Machine Learning Research, 11, 625-
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and
Machines, 30(4), 681-694.
Goh, G., Cammarata, N., Voss, C., Carter, S., Petrov, M., Schubert, L., Radford, A., & Olah, C.
(2021). Multimodal neurons in artificial neural networks. Retrieved from
https://doi.org/10.23915/distill.00030
King, M. R. (2022). The future of AI in medicine: A perspective from a chatbot. Annals of
Biomedical Engineering. https://doi.org/10.1007/s10439-022-03121-w
Kirmani, A. R. (2022). Artificial intelligence-enabled science poetry. ACS Energy Letters, 8,
-576.
Lee, C., Panda, P., Srinivasan, G., & Roy, K. (2018). Training deep spiking convolutional neural
networks with STDP-based unsupervised pre-training followed by supervised fine-tuning.
Frontiers in Neuroscience, 12, article 435.
Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., & Tang, J. (2021). GPT understands,
too. arXiv. https://doi.org/10.48550/arXiv.2103.10385
Lucy, L., & Bamman, D. (2021). Gender and representation bias in GPT-3 generated stories.
Proceedings of the Workshop on Narrative Understanding, 3, 48-55.
MacNeil, S., Tran, A., Mogil, D., Bernstein, S., Ross, E., & Huang, Z. (2022). Generating
diverse code explanations using the GPT-3 large language model. Proceedings of the ACM
Conference on International Computing Education Research, 2, 37-39.
Manning, C., & Schutze, H. (1999). Foundations of statistical natural language processing. MIT
Press.
Marcus, G., Davis, E., & Aaronson, S. (2022). A very preliminary analysis of DALL-E 2. ArXiv
pre-print. Retrieved from https://doi.org/10.48550/arXiv.2204.13807
Mollman, S. (2022). ChatGPT gained 1 million users in under a week. Retrieved from
https://www.yahoo.com/lifestyle/chatgpt-gained-1-million-followers
Niu, Z., Zhong, G., & Yu, H. (2021). A review on the attention mechanism of deep learning.
Neurocomputing, 452, 48-62.
OpenAI. (2022). OpenAI about page. Retrieved from https://openai.com/about/
Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative
artificial intelligence for journalism and media education. Journalism and Mass Communication
Educator. https://doi.org/10.1177/10776958221149577
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language
understanding by generative pre-training. Retrieved from
https://www.cs.ubc.ca/~amuham01/LING530/papers/radford2018improving.pdf
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep
learning in NLP. Proceedings of the Annual Meeting of the Association for Computational
Linguistics, 57, 3645-3650.
Zhou, X., Chen, Z., Jin, X., & Wang, W. Y. (2021). HULK: An energy efficiency benchmark
platform for responsible natural language processing. Proceedings of the Conference of the
European Chapter of the Association for Computational Linguistics: System Demonstrations, 16,
-336.
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Copyright (c) 2024 Marchel, Septi (Author); Pratama (Translator)
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