Guest Editors
Assoc. Prof. Jia-Wei Chang
Email: jwchang@nutc.edu.tw
Affiliation: National Taichung University of Science and Technology, Taichung, 400, Taiwan
Homepage:
Research Interests: natural language processing, internet of things, artificial intelligence, data mining, and e-learning technologies
Assoc. Prof. Chih-Chieh Hung
Email: smalloshin@nchu.edu.tw
Affiliation: National Chung Hsing University, Taichung, 400, Taiwan
Homepage:
Research Interests: intelligent traffic systems, AI-empowered Design, Fintech, spatiotemporal database, big data anayltics, deep reinforcement learning, data mining and artificial intelligence
Summary
The rapid evolution of artificial intelligence (AI) has been significantly propelled by advancements in Natural Language Processing (NLP) and the development of Large Language Models (LLMs). With its focus on algorithmic modeling and language-specific tasks, NLP has been instrumental in refining AI’s ability to understand and process human language. Meanwhile, LLMs, with their vast pre-training on extensive datasets, have brought about unprecedented contextual understanding and generative capabilities. As these two fields mature, their integration presents an unparalleled opportunity to enhance AI systems, enabling them to perform more complex tasks with higher accuracy, efficiency, and adaptability. Integrating NLP with LLMs represents a significant leap forward in developing advanced language processing systems. By merging NLP’s precision in handling specific language tasks with LLM’s expansive contextual understanding, this synergy promises to enhance the accuracy, efficiency, and adaptability of AI systems.
This special issue seeks to delve into the synergistic potential of combining NLP and LLM technologies to push the boundaries of what AI can achieve across various industries. We invite researchers, practitioners, and industry experts to submit original research papers that explore the following (but not limited to) topics:
• Enhanced Accuracy and Contextual Understanding: Explore how integrating NLP and LLMs improves accuracy in language tasks like understanding, generation, and translation.
• Resource Optimization: Examine strategies for optimizing or reducing computational resources by leveraging NLP and LLMs for more efficient solutions.
• Flexibility and Adaptability in AI Applications: Investigate how combining NLP and LLMs enhances AI systems' flexibility and responsiveness to evolving needs.
• Real-World Integration and Case Studies: Develop NLP and LLM integrations in sectors like healthcare, highlighting improvements in performance and satisfaction.
• Ethical Considerations and Bias Mitigation: Research on addressing ethical challenges and mitigating biases in AI systems that integrate NLP and LLMs, ensuring fairness and transparency in AI-driven decisions.
• Cross-Language and Multilingual Applications: Studies on the application of NLP and LLM integration in multilingual settings, improving cross-language understanding and translation.
• User-Centric AI Interactions: Research on how NLP and LLM integration can be leveraged to create more intuitive and user-friendly AI interfaces, improving user experience and engagement.
• Security and Privacy in NLP and LLM Integration: Studies on ensuring the security and privacy of data in AI systems that integrate NLP and LLMs, particularly in sensitive applications like healthcare and finance.
• Innovations in Language Understanding for IoT and Edge Computing: Research on applying NLP and LLM integration to IoT and edge computing environments, enhancing language understanding in decentralized systems.
• Future Directions and Predictive Studies: Explore the future potential of NLP and LLMs, focusing on advancements in AI assistants, content creation, and robotics.
Keywords
NLP, LLMs, Resource Optimization, AI, IoT and Edge Computing
Published Papers
Login
Register
Submit a Paper
Propose a Special lssue