Innovative Content Generation: Leveraging GPT-3 Language Capabilities
DOI:
https://doi.org/10.69478/JITC2023v5n2a04Keywords:
GPT-3, Natural Language Processing, Content generation, Text generation, AI language modelsAbstract
This study explores the utilization of OpenAI's GPT-3 in innovative content generation, leveraging its unparalleled language capabilities. GPT-3 represents a milestone in AI development, building upon earlier models like BERT and OpenAI's GPT series, propelled by the transformer architecture. The study aims to unravel the mechanisms underlying GPT-3's content creation process, assessing its strengths, limitations, and optimal use cases. By examining its efficacy across various domains and genres, from articles to poetry, GPT-3's versatility and potential impact are showcased. Ethical considerations surrounding AI-generated content are also explored. The investigation underscores the transformative potential of GPT-3 in augmenting content creation practices worldwide, where AI-powered tools complement human creativity, fostering innovation and expression.
References
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is All You Need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17), December 2017, pp. 6000-6010.
Sciforce, “What is GPT-3, How Does It Work, and What Does It Actually Do?,” https://medium.com/sciforce/what-is-gpt-3-how-does-it-work-and-what-does-it-actually-do-9f721d69e5c1.
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, June 2029, pp. 4171–4186, https://doi.org/10.18653/v1/N19-1423.
S. Baranwal, “Understanding BERT,” https://pub.towardsai.net/understanding-bert-b69ce7ad03c1.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, and P. J. Liu, “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer,” Journal of Machine Learning Research, vol. 21, 2020, pp. 1-67.
J. Howard and S. Ruder, “Universal Language Model Fine-tuning for Text Classification,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1, January 2018, https://doi.org/10.18653/v1/P18-1031.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, and Q. V. Le, “XLNet: Generalized Autoregressive Pre-training for Language Understanding,” in Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019, pp. 5753-5763.
Elvis, “XLNet Outperforms BERT on Several NLP Tasks”, Medium, https://medium.com/dair-ai/xlnet-outperforms-bert-on-several-nlp-tasks-9ec867bb563b.
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, V. Stoyanov, “RoBERTa: A Robustly Optimized BERT Pretraining Approach”, ArXiv, https://www.arxiv.org/abs/1907.11692.
K. Khumari, “RoBERTa: A Modified BERT Model for NLP”, Comet, https://www.comet.com/site/blog/roberta-a-modified-bert-model-for-nlp/.
Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. V. Le, and R. Salakhutdinov, “Transformer-XL: Attentive Language Models Beyond a Fixed-length Context”, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, July 28- August 2, 2019, pp. 2978-2988.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan et al., “Language Models Are Few-shot Learners”, https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html.
N. S. Keskar, B. McCann, L. R. Varshney, C. Xiong, and R. Socher, “CTRL: A Conditional Transformer Language Model for Controllable Generation”, ArXiv, https://www.arxiv.org/abs/1909.05858.
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, https://doi.org/10.1145/3442188.3445922.
A. Radford and K. Narasimhan, “Improving Language Understanding by Generative Pre-training”, https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf.
Y. Zhang, S. Liu, Y. Zhang, and X. Wang, “ChatGPT: A Comprehensive Review on Background, Applications, Key Challenges, Bias, Ethics, Limitations and Future Scope”, Internet of Things and Cyber-Physical Systems, vol. 3, April 2023, pp. 121-154, https://doi.org/10.1016/j.iotcps.2023.04.003.
R. Dathathri, A. Madotto, J. Lan, J. Hung, E. Frank, P. Molino, J. Yosinski, and R, Liu “Plug and Play Language Models: A Simple Approach to Controlled Text Generation”, https://openreview.net/pdf?id=H1edEyBKDS.
Sanki, “OpenAI GPT-3: A New Era in Natural Language Processing”, Medium, https://medium.com/@sanket.ai/openai-gpt-3-a-new-era-in-natural-language-processing-31afb2747507.
Downloads
Published
License
Copyright (c) 2023 Kenette Roeven C. Saylo, Monalie C. Saylo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
