
An Interdisciplinary Exploration of Concept and Application of Large Language Models
- 1 Tianjin University
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Abstract
Large Language Models (LLMs) have emerged as transformative tools in Artificial Intelligence (AI), fueled by advancements in deep learning. Notably, OpenAI's Generative Pretrained Transformer (GPT) series has showcased their capacity to comprehend and generate human-like text, making them indispensable across various domains. This paper provides a comprehensive exploration of LLMs, encompassing their foundational principles, technical advantages, and multifaceted applications spanning agriculture, medicine, and information security. By elucidating how LLMs revolutionize these sectors through heightened efficiency, accuracy, and innovation, this work unveils their potential to reshape industries and drive technological progress. Additionally, this work delves into forthcoming prospects and potential challenges in LLM development and deployment, concluding with a synopsis of pivotal insights. As LLMs continue to evolve, their integration into diverse fields promises profound implications for human-computer interaction and societal advancement. This paper illuminates the trajectory of LLMs, from their inception to their current prominence, underscoring their pivotal role in shaping the future of AI and fostering responsible innovation.
Keywords
Large Language Models, Artificial Intelligence, Deep Learning
[1]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., et al: Attention is all you need. Advances in neural information processing systems, vol. 30, pp. 5998-6008. The MIT Press, Long Beach (2017).
[2]. Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., et al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023).
[3]. LeCun, Y., Bengio, Y., & Hinton, G.: Deep learning. nature, 521(7553), 436-444 (2015).
[4]. Shrestha, A., & Mahmood, A.: Review of deep learning algorithms and architectures. IEEE access, 7, 53040-53065 (2019).
[5]. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., et al.: Language models are few-shot learners. Advances in neural information processing systems, vol. 33, pp. 1877-1901. The MIT Press, Virtual (2020).
[6]. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I.: Language models are unsupervised multitask learners. OpenAI blog, 1(8), 1-9 (2019).
[7]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[8]. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639), 115-118 (2017).
[9]. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., et al.: Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489 (2016).
[10]. Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234-1240 (2020).
[11]. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
[12]. Bizer, C., Heath, T., & Berners-Lee, T.: Linked data-the story so far. In Linking the World’s Information: Essays on Tim Berners-Lee’s Invention of the World Wide Web, 115-143 (2023).
Cite this article
Ji,Z. (2025). An Interdisciplinary Exploration of Concept and Application of Large Language Models. Applied and Computational Engineering,133,8-15.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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