ChatGPT and Open-AI Models: A Preliminary Review

Author:

Roumeliotis Konstantinos I.1ORCID,Tselikas Nikolaos D.1ORCID

Affiliation:

1. Department of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, Greece

Abstract

According to numerous reports, ChatGPT represents a significant breakthrough in the field of artificial intelligence. ChatGPT is a pre-trained AI model designed to engage in natural language conversations, utilizing sophisticated techniques from Natural Language Processing (NLP), Supervised Learning, and Reinforcement Learning to comprehend and generate text comparable to human-generated text. This article provides an overview of the training process and fundamental functionality of ChatGPT, accompanied by a preliminary review of the relevant literature. Notably, this article presents the first comprehensive literature review of this technology at the time of publication, aiming to aggregate all the available pertinent articles to facilitate further developments in the field. Ultimately, the authors aim to offer an appraisal of the technology’s potential implications on existing knowledge and technology, along with potential challenges that must be addressed.

Publisher

MDPI AG

Subject

Computer Networks and Communications

Reference95 articles.

1. Brown, T.B., Mann, B., and Ryder, N. (2020). Language Models are Few-Shot Learners. arXiv.

2. Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H.P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., and Brockman, G. (2021). Evaluating large language models trained on code. arXiv.

3. Wahde, M., and Virgolin, M. (2022). Conversational agents: Theory and applications. arXiv.

4. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2023, April 26). Language Models Are Unsupervised Multitask Learners. OpenAI Blog. Available online: https://life-extension.github.io/2020/05/27/GPT%E6%8A%80%E6%9C%AF%E5%88%9D%E6%8E%A2/language-models.pdf.

5. Wei, J., Bosma, M., Zhao, V.Y., Guu, K., Yu, A.W., Lester, B., Du, N., Dai, A.M., and Le, Q.V. (2022). Finetuned language models are zero-shot learners. arXiv.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3