Generative Pre-trained Transformer: A Comprehensive Review on Enabling
Technologies, Potential Applications, Emerging Challenges, and Future
Directions
- URL: http://arxiv.org/abs/2305.10435v2
- Date: Sun, 21 May 2023 10:12:02 GMT
- Title: Generative Pre-trained Transformer: A Comprehensive Review on Enabling
Technologies, Potential Applications, Emerging Challenges, and Future
Directions
- Authors: Gokul Yenduri, Ramalingam M, Chemmalar Selvi G, Supriya Y, Gautam
Srivastava, Praveen Kumar Reddy Maddikunta, Deepti Raj G, Rutvij H Jhaveri,
Prabadevi B, Weizheng Wang, Athanasios V. Vasilakos, and Thippa Reddy
Gadekallu
- Abstract summary: The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing.
GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks.
- Score: 11.959434388955787
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Generative Pre-trained Transformer (GPT) represents a notable
breakthrough in the domain of natural language processing, which is propelling
us toward the development of machines that can understand and communicate using
language in a manner that closely resembles that of humans. GPT is based on the
transformer architecture, a deep neural network designed for natural language
processing tasks. Due to their impressive performance on natural language
processing tasks and ability to effectively converse, GPT have gained
significant popularity among researchers and industrial communities, making
them one of the most widely used and effective models in natural language
processing and related fields, which motivated to conduct this review. This
review provides a detailed overview of the GPT, including its architecture,
working process, training procedures, enabling technologies, and its impact on
various applications. In this review, we also explored the potential challenges
and limitations of a GPT. Furthermore, we discuss potential solutions and
future directions. Overall, this paper aims to provide a comprehensive
understanding of GPT, enabling technologies, their impact on various
applications, emerging challenges, and potential solutions.
Related papers
- A Survey on Large Language Models from Concept to Implementation [4.219910716090213]
Recent advancements in Large Language Models (LLMs) have broadened the scope of natural language processing (NLP) applications.
This paper investigates the multifaceted applications of these models, with an emphasis on the GPT series.
This exploration focuses on the transformative impact of artificial intelligence (AI) driven tools in revolutionizing traditional tasks like coding and problem-solving.
arXiv Detail & Related papers (2024-03-27T19:35:41Z) - From Generative AI to Generative Internet of Things: Fundamentals,
Framework, and Outlooks [82.964958051535]
Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making.
By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society.
arXiv Detail & Related papers (2023-10-27T02:58:11Z) - Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI)
We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
arXiv Detail & Related papers (2023-07-05T06:03:40Z) - A Comprehensive Survey on Applications of Transformers for Deep Learning
Tasks [60.38369406877899]
Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data.
transformer models excel in handling long dependencies between input sequence elements and enable parallel processing.
Our survey encompasses the identification of the top five application domains for transformer-based models.
arXiv Detail & Related papers (2023-06-11T23:13:51Z) - Interactive Natural Language Processing [67.87925315773924]
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP.
This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
arXiv Detail & Related papers (2023-05-22T17:18:29Z) - Prompt Engineering for Healthcare: Methodologies and Applications [93.63832575498844]
This review will introduce the latest advances in prompt engineering in the field of natural language processing for the medical field.
We will provide the development of prompt engineering and emphasize its significant contributions to healthcare natural language processing applications.
arXiv Detail & Related papers (2023-04-28T08:03:42Z) - ChatGPT: Applications, Opportunities, and Threats [0.0]
ChatGPT is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques.
The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations.
Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human.
arXiv Detail & Related papers (2023-04-14T16:25:03Z) - ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large
Language Models in Multilingual Learning [70.57126720079971]
Large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP)
This paper evaluates ChatGPT on 7 different tasks, covering 37 diverse languages with high, medium, low, and extremely low resources.
Compared to the performance of previous models, our extensive experimental results demonstrate a worse performance of ChatGPT for different NLP tasks and languages.
arXiv Detail & Related papers (2023-04-12T05:08:52Z) - Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in
geotechnical engineering [2.132096006921048]
GPT models can generate plausible-sounding but false outputs, leading to hallucinations.
By integrating GPT into geotechnical engineering, professionals can streamline their work and develop sustainable and resilient infrastructure systems.
arXiv Detail & Related papers (2023-04-04T21:47:41Z) - Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing [4.13365552362244]
ChatGPT has been successfully applied in numerous areas, including chatbots, content generation, language translation, personalized recommendations, and even medical diagnosis and treatment.
Its success in these applications can be attributed to its ability to generate human-like responses, understand natural language, and adapt to different contexts.
This article provides a comprehensive overview of ChatGPT, its applications, advantages, and limitations.
arXiv Detail & Related papers (2023-03-27T21:27:58Z) - A Survey of Controllable Text Generation using Transformer-based
Pre-trained Language Models [21.124096884958337]
Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG)
We present a systematic critical review on the common tasks, main approaches, and evaluation methods in this area.
We discuss the challenges that the field is facing, and put forward various promising future directions.
arXiv Detail & Related papers (2022-01-14T08:32:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.