Submission Deadline: 31 October 2026 View: 1800 Submit to Special Issue
Prof. Liang-Chih Yu
Email: lcyu@saturn.yzu.edu.tw
Affiliation: Department of Information Management, Yuan Ze University, Taoyuan City, 32003, Taiwan
Research Interests: natural language processing, sentiment analysis, text mining, learning technology
Prof. Lung-Hao Lee
Email: lhlee@nycu.edu.tw
Affiliation: Institute of Artificial Intelligence Innovation, National Yang Ming Chiao Tung University, Taipei City, 112304, Taiwan
Research Interests: natural language processing, medical language understanding, information retrieval and extraction, dimensional sentiment analysis
Prof. Yi-Cheng Chen
Email: ycchen@mgt.ncu.edu.tw
Affiliation: Department of Information Management, National Central University, Taoyuan City, 320317, Taiwan
Research Interests: data mining, social network analysis, data acquisition, smart home, cloud computing
Recent advances in Large-scale AI Models, particularly Large Language Models (LLMs), have transformed the landscape of Natural Language Processing (NLP) by enabling unprecedented capabilities in understanding, reasoning, and generation across languages and modalities. As these models expand to multimodal and domain-specific applications, they bring new opportunities for innovation as well as challenges in efficiency, safety, interpretability, and ethical deployment.
This special issue aims to bring together cutting-edge research on the design, training, evaluation, and application of LLMs and other large-scale AI models in NLP and beyond. It welcomes contributions that advance theoretical understanding, propose novel architectures, enhance robustness, or explore real-world applications.
Potential topics include, but are not limited to the following:
· Efficient training and deployment of LLMs
· Safety, Ethics, and Alignment in LLMs
· Interpretability, Model Editing, and Transferable AI
· LLM Agents and Knowledge-augmented AI Systems
· Human-AI Collaboration Beyond Dialogue
· Multilingual and Multimodal Large-scale Models
· Sentiment Analysis and Computational Social Science
· Domain-specific and Low-resource Applications
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