Full Text:   <2926>

Summary:  <443>

CLC number: TP181

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2023-09-18

Cited: 0

Clicked: 2661

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Bing LI

https://orcid.org/0000-0002-1251-4346

Peng YANG

https://orcid.org/0000-0002-1184-8117

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.1 P.64-83

http://doi.org/10.1631/FITEE.2300410


Advances and challenges in artificial intelligence text generation


Author(s):  Bing LI, Peng YANG, Yuankang SUN, Zhongjian HU, Meng YI

Affiliation(s):  School of Computer Science and Engineering, Southeast University, Nanjing 210000, China; more

Corresponding email(s):   libing@seu.edu.cn, pengyang@seu.edu.cn, syk@seu.edu.cn, huzj@seu.edu.cn

Key Words:  AI text generation, Natural language processing, Machine learning, Deep learning



Abstract: 
Text generation is an essential research area in artificial intelligence (AI) technology and natural language processing and provides key technical support for the rapid development of AI-generated content (AIGC). It is based on technologies such as natural language processing, machine learning, and deep learning, which enable learning language rules through training models to automatically generate text that meets grammatical and semantic requirements. In this paper, we sort and systematically summarize the main research progress in text generation and review recent text generation papers, focusing on presenting a detailed understanding of the technical models. In addition, several typical text generation application systems are presented. Finally, we address some challenges and future directions in AI text generation. We conclude that improving the quality, quantity, interactivity, and adaptability of generated text can help fundamentally advance AI text generation development.

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