Artificial intelligence (AI) has emerged as a critical force in today's technological environment, impacting many aspects of daily life. One of the most significant advances in this field is OpenAI's creation of Generative Pre-trained Transformer (GPT) models, which have changed the way humans engage with digital platforms (Vaswani et al., 2017).

 

The Generative Pre-trained Transformer (GPT) is a well-known family of neural network models that specialize in natural language processing. GPT models, which were first developed by OpenAI in 2018, use a transformer architecture and are pre-trained on huge text corpora (plural-corpus is a collection of texts) to deliver robust language creation and understanding. Over iterations, the models' scale has risen substantially, resulting in more capable performance.

 

GPT models have a wide range of applications in a variety of industries. They power chatbots that deliver human-like responses around the clock in customer service (OpenAI, 2021). GPT aids in the generation of innovative and informative prose, speeding the writing process (OpenAI, 2021). These models are also important in language translation, as they improve cross-cultural communication (Brown et al., 2020).

 

The following table summarizes key versions of the GPT models:

 

Model	Developer	Year	Parameters	Training Data	Key Notes	References
GPT-1	OpenAI	2018	117M	BookCorpus	Original GPT model	(Radford et al., 2018)
GPT-2	OpenAI	2019	1.5B	WebText	Strong text generation	(Radford et al., 2019)
GPT-3	OpenAI	2020	175B	Common Crawl	Impressive few-shot learning	(Brown et al., 2020)
GPT-3.5	Anthropic	2022	20B	-	Faster inference than GPT-3	(Anthropic, 2022)
GPT-4	Anthropic	2022	100T (expected)	-	Next generation model, not released yet	(Metz, 2022)
GPT-3.5 Turbo	Anthropic	2023	7.5B	-	Faster version of GPT-3.5	(Anthropic, 2023a)
GPT-4 Dragon	Anthropic	2023	100T	-	Expanded GPT-4 for researchers	(Anthropic, 2023b)
GPT-3.6	OpenAI	2023	10B	-	Upgrade over GPT-3	(OpenAI, 2023)
GPT-4 Muse	Anthropic	2023	200B	-	GPT-4 focused on creativity	(Anthropic, 2023c)

 

As shown in the table, the GPT models have rapidly scaled up in size and capability with each new version. Both OpenAI and Anthropic are pushing the boundaries of large language models with their ongoing GPT research and development. Exciting advancements in natural language processing abilities are anticipated as these models continue to evolve.

 

REFERENCES

1. Anthropic. (2022). Anthropic AI research lab tackles high-stakes problems using self-correcting AI. https://www.anthropic.com
2. Anthropic. (2023a). Introducing GPT-3.5 Turbo. https://www.anthropic.com/blog/announcements/introducing-gpt-3-5-turbo
3. Anthropic. (2023b). Introducing GPT-4 Dragon. https://www.anthropic.com/blog/announcements/introducing-gpt-4-dragon 
4. Anthropic. (2023c). Introducing GPT-4 Muse. https://www.anthropic.com/blog/announcements/introducing-gpt-4-muse
5. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., ... Amodei, D. (2020). Language models are few-shot learners. https://arxiv.org/abs/2005.14165
6. Metz, C. (2022, April 11). A.I. researchers are making more than just code. The New York Times. https://www.nytimes.com/2022/04/11/technology/ai-anthropic-research-lab.html
7. OpenAI. (2023). OpenAI API: GPT-3 upgrades. https://platform.openai.com/docs/guides/gpt-3/upgrades
8. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
9. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
10. Vaswani, A., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems.

 

Prepared by:
Hashimah Amat Sejani

Date of Input: 28/12/2023 | Updated: 28/12/2023 | nazlina