Advanced Large Language Models and Natural Language Processing Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 1559
Special Issue Editors
Interests: computational intelligence; neural networks; optimization
Special Issues, Collections and Topics in MDPI journals
Interests: spatio-temporal computing; optimization and control; AI applications in transportation
Special Issue Information
Dear Colleagues,
The rapid growth of big data and the advancements in computational power have spurred remarkable progress in the field of natural language processing (NLP). Central to this progress are large language models (LLMs) like GPT, BERT, and T5, which have shown exceptional capabilities in understanding and generating human-like text. These models, a subset of artificial intelligence (AI), leverage deep learning techniques to build predictive models that can handle diverse NLP tasks such as language translation, summarization, and sentiment analysis. LLMs and NLP applications have revolutionized various industries, from automating customer support to enhancing content generation and improving healthcare analytics.
However, despite their impressive performance, challenges remain in the interpretability, scalability, and ethical implications of these models. This Special Issue aims to bring together researchers and practitioners to discuss and exchange the latest advancements in large language models and NLP applications. We welcome original research articles and comprehensive reviews that provide insights into this rapidly evolving field. Potential topics include, but are not limited to, the following: training and fine-tuning of large language models; efficient architectures and optimization techniques for LLMs; multilingual and cross-lingual LLMs; summarization, translation, and question-answering systems; zero-shot and few-shot learning in NLP; text generation and creative writing using LLMs; integration of LLMs with computer vision and multimodal learning; ethical, societal, and interpretability challenges of LLMs; robustness and security issues in large language models; and data privacy and bias mitigation in NLP applications.
Dr. Man-Fai Leung
Dr. Duo Li
Dr. Jin Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- NLP
- large language models
- text generation
- sentiment analysis
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