Research on the Innovative Framework and Application of Large Language Models in Cross Modal Tasks

As a focus in the field of artificial intelligence in recent years, Large Language Models (LLMs) have the ability to generate human readable natural language by learning massive amounts of text data, and have achieved remarkable results in natural language processing tasks such as machine translation, dialogue generation, and question answering systems. However, its development has not been smooth sailing, facing many challenges such as lack of reasoning ability, bias and bias, and weak model interpretability. Finally, looking forward to the future development trends of big language models, including improving generalization ability, enhancing interpretability, and innovative ideas in privacy protection, ethical security, and other aspects. Intended to provide comprehensive and in-depth analysis and recommendations for the research and application of large language models, helping to promote the continuous progress of natural language processing technology.

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Published
2025-07-11
DOI
https://doi.org/10.1109/cisat66811.2025.11181914
Primary Topic
Natural Language Processing Techniques
Type
article
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0.00
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article

Research on the Innovative Framework and Application of Large Language Models in Cross Modal Tasks

Guohua Zhu, Yifan Wang, Y.T. Ji, Yaoxuan Liang et al.
Natural Language Processing Techniques
article

Research on the Innovative Framework and Application of Large Language Models in Cross Modal Tasks

Guohua Zhu, Yifan Wang, Y.T. Ji, Yaoxuan Liang, Zhen Tian
article en

Abstract

As a focus in the field of artificial intelligence in recent years, Large Language Models (LLMs) have the ability to generate human readable natural language by learning massive amounts of text data, and have achieved remarkable results in natural language processing tasks such as machine translation, dialogue generation, and question answering systems. However, its development has not been smooth sailing, facing many challenges such as lack of reasoning ability, bias and bias, and weak model interpretability. Finally, looking forward to the future development trends of big language models, including improving generalization ability, enhancing interpretability, and innovative ideas in privacy protection, ethical security, and other aspects. Intended to provide comprehensive and in-depth analysis and recommendations for the research and application of large language models, helping to promote the continuous progress of natural language processing technology.

Shandong University (CN), Hebei University of Technology (CN), East University Of Heilongjiang (CN), Southwest Jiaotong University (CN), University of Glasgow (GB)
Openalex Percentile: Top 86%
Natural Language Processing Techniques
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Research on the Innovative Framework and Application of Large Language Models in Cross Modal Tasks — Guohua Zhu, Yifan Wang, et al. · (2025) | TGRS Research Map | TGRS