Practical Applications of Large Language Models in Natural Language Processing
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 April 2026 | Viewed by 17
Special Issue Editors
Interests: artificial intelligence; natural language processing; knowledge graph; large language model
Interests: natural language processing; knowledge graph; multimodal learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the rapidly evolving landscape of artificial intelligence and computational linguistics, Large Language Models (LLMs) have emerged as transformative tools that are revolutionizing how we approach natural language processing tasks. The unprecedented capabilities of these models in understanding, generating, and manipulating human language have opened new frontiers in both theoretical research and practical applications across diverse domains. However, the successful deployment of LLMs in real-world scenarios presents unique challenges related to computational efficiency, domain adaptation, ethical considerations, and integration with existing systems. This Special Issue aims to bridge the gap between cutting-edge LLM research and practical implementation by showcasing innovative approaches that demonstrate the tangible benefits and solutions that these models provide to natural language processing challenges.
We invite the submission of original research contributions including, but not limited to, the following areas: LLM-powered textual analysis and understanding; domain-specific fine-tuning and adaptation; information extraction and knowledge graph construction; advanced conversational AI and personalized user experiences; applications in specialized sectors such as law, healthcare, and software engineering; efficient deployment strategies, including retrieval-augmented generation (RAG) and model compression; ethical AI, focusing on fairness, explainability, and bias mitigation; and the integration of LLMs with external tools and traditional NLP pipelines. Advancements in these areas promise to accelerate the adoption of intelligent language technologies, leading to more sophisticated and accessible natural language processing solutions for diverse industries and applications.
Dr. Yongrui Chen
Prof. Dr. Guilin Qi
Guest Editors
Dr. Hui Yang
Guest Editor Assistant
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Keywords
- large language models
- applied natural language processing
- domain adaptation
- efficient deployment
- ethical AI
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