Advanced Applications of Large Language Models
A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 February 2027 | Viewed by 146
Editors
Interests: large language models; natural language processing; deep learning; AI for science
Special Issues, Collections and Topics in MDPI journals
Interests: AI safety; natural language processing; knowledge intelligence
Special Issue Information
Dear Colleagues,
Large Language Models (LLMs) have rapidly evolved from general-purpose language understanding and generation systems into foundational technologies that are reshaping scientific research, industrial applications, and human–computer interaction. Recent advances in reasoning, instruction following, tool use, code generation, and multimodal understanding have significantly expanded the scope of LLMs, enabling them to address increasingly complex real-world problems across diverse domains.
Beyond improving model capabilities, the next frontier lies in developing advanced LLM-driven applications that integrate domain knowledge, external tools, multimodal information, and collaborative intelligence. Emerging paradigms such as agentic LLMs, multi-agent systems, coding assistants, and domain-specific foundation models are transforming LLMs from standalone conversational systems into intelligent platforms capable of autonomous planning, decision-making, scientific discovery, software engineering, and task execution. These developments are accelerating innovation in fields including healthcare, education, finance, law, manufacturing, robotics, and AI for Science.
This Special Issue aims to provide a forum for researchers and practitioners from academia and industry to present the latest advances in the design, deployment, and evaluation of advanced LLM applications. We particularly encourage contributions that demonstrate how LLMs can be effectively combined with multimodal perception, external knowledge, software tools, autonomous agents, and domain expertise to solve practical challenges and create intelligent systems.
Dr. Min Yang
Dr. Ziyu Lyu
Prof. Dr. Chengming Li
Guest Editors
Manuscript Submission Information
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Keywords
- large language models (LLMs)
- LLM applications
- multimodal LLMs (MLLMs)
- coding LLMs and AI-assisted software engineering
- agentic LLMs
- multi-agent LLM systems
- agent harness engineering
- tool learning and tool-using LLMs
- vertical-domain LLMs
- LLMs for scientific discovery
- human–AI collaboration
- safety, alignment, and responsible deployment of LLM applications
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