Large Language Models: Methods and Applications
Deadline for manuscript submissions: 31 July 2024 | Viewed by 6133
2. Automated Systems & Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 12435, Saudi Arabia
3. Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
Interests: artificial intelligence; robotics; control theory and applications; machine learning; computational intelligence
Special Issues, Collections and Topics in MDPI journals
2. School of Computing and Information Systems, The University of Melbourne, Melbourne 3010, Australia
Interests: biomedical natural language processing; computational linguistics; text mining; health informatics; computational biology
Special Issues, Collections and Topics in MDPI journals
In recent years, large language models have emerged as transformative tools in the fields of natural language processing and artificial intelligence. These models, powered by deep learning techniques, transformer architectures leveraging attention, and vast amounts of training data, have revolutionized the way we understand and interact with human language. Their applications span across various domains, including machine translation, text generation, sentiment analysis, question answering, summarization, and much more. As the capabilities of these models continue to expand, it has become increasingly essential to examine the methods behind their development and explore the diverse range of applications that leverage their power.
This Special Issue, titled "Large Language Models: Methods and Applications," aims to provide a comprehensive overview of the latest advancements in the realm of large language models. It aims to bring together researchers, practitioners, and experts from various fields to delve into the intricacies of these models and showcase their broad spectrum of applications. Whether you are a seasoned professional seeking to deepen your understanding of these models or a newcomer curious about their potential, this collection of articles will offer valuable insights.
The Special Issue will encompass a variety of topics, including the architecture and training methodologies of large language models, critical evaluations of their capabilities and limitations, algorithmic approaches to improving their computational and energy efficiency, the ethical considerations surrounding their training or deployment, and their applications in areas such as natural language understanding, content generation, and conversational AI. Each article in this Special Issue provides a unique perspective, contributing to the overall discourse on the methods and applications of large language models.
The articles in this Special Issue not only aim to explore the current state of the art but also provide a glimpse into the future possibilities and challenges associated with these models. Whether you are a researcher seeking inspiration for your next project or a professional eager to harness the potential of large language models for practical applications, the knowledge and insights shared in this Special Issue will prove invaluable.
We invite you to embark on this journey through the world of large language models as we examine the methods that underpin their remarkable capabilities and explore the diverse applications that are shaping the future of natural language processing and artificial intelligence.
We hope that this Special Issue serves as a valuable resource for researchers, practitioners, policymakers, and anyone interested in the exciting advances enabled through large language models.
Prof. Dr. Ahmad Taher Azar
Prof. Dr. Karin Verspoor
Prof. Dr. Irena Spasic
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- artificial intelligence
- advanced LLM architectures
- BERT Applications (Bidirectional Encoder Representations from Transformers)
- complexity of LLMs
- computational/energy efficiency
- deep reinforcement learning
- deep generative models
- ethics of LLM systems and applications
- evaluation of LLM performance
- language understanding
- machine translation
- NLP (Natural Language Processing)
- sentiment analysis
- text generation
- transformer architectures
- transfer learning
- unsupervised learning
- attention mechanisms
- data augmentation
- GPT (Generative Pretrained Transformer)
- multimodal models
- zero-shot learning