Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

<Previous Article In Volume
Next Article In Volume>

Comprehensive to the Textual Hallucination in Generative AI

Authors
Yiyang Li1, *
1College of Computer Science, Sichuan University, Chengdu, Sichuan, China
*Corresponding author. Email: 2023141460145@stu.scu.edu.cn
Corresponding Author
Yiyang Li
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_37How to use a DOI?
Keywords
Textual Hallucination; Factual Consistency; Mitigation Strategies
Abstract

Generative AI has been particularly strong in many places in recent years, especially large language models that have done very well in writing articles, answering questions, and helping to learn these things. However, these models sometimes make mistakes, such as making up factual content, or giving answers that have no evidence to support or even logical confusion, which do bring a lot of trouble in actual use. This paper has carefully sorted out the current research on the illusion of generative models, and proposed a new classification method, which is based on several aspects. This paper also analyzes why these hallucinations occur, the main reason may be related to the training data, or it may be the problem of the model training, or the reasoning process is wrong, and even when people and machines interact with it. To improve this situation, there are some methods that are being tried, such as making more detailed adjustments to the model, finding ways to make the model more knowledgeable, improving the reasoning process, and combining search techniques to help generate better content.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_37How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yiyang Li
PY  - 2026
DA  - 2026/04/24
TI  - Comprehensive to the Textual Hallucination in Generative AI
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 339
EP  - 346
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_37
DO  - 10.2991/978-94-6239-648-7_37
ID  - Li2026
ER  -