AI-Powered Natural Language Processing Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 August 2026 | Viewed by 10
Special Issue Editor
Special Issue Information
Dear Colleagues,
Recent advances in large language models have significantly reshaped the field of Natural Language Processing (NLP) and its industrial applications. From generative conversational agents to multimodal interaction systems and domain-specific text analytics, AI-powered NLP has become a key enabler of digital transformation across various sectors, including finance, healthcare, education, e-commerce, law, and social computing. The next phase of AI-powered NLP demands more trustworthy, secure, robust, efficient, and context-aware techniques that can seamlessly integrate domain knowledge, multimodal signals, human feedback, and real-world constraints. In this Special Issue, we are particularly interested in exploring, characterizing, and evaluating emerging AI-driven methodologies in NLP and presenting innovative models, datasets, tools, benchmarks, and applications that demonstrate measurable impact in academic, industrial, or societal contexts.
Topics of interest include, but are not limited to, the following:
- Foundation models / LLMs for text understanding, reasoning, and generation;
- Multimodal language processing (e.g., speech–vision–text fusion);
- Human-in-the-loop NLP, prompt engineering, and preference alignment;
- Domain-specific or verticalized NLP (e.g., legal NLP, financial NLP, medical NLP, and NLP for software engineering);
- Responsible and trustworthy NLP: fairness, safety, transparency, privacy, accountability, and explainability;
- Efficient, compact, or on-device NLP models and inference optimization;
- Benchmarking, evaluation metrics, and reproducibility in NLP systems;
- High-value industrial applications of NLP and generative AI.
Dr. Weibin Wu
Guest Editor
Manuscript Submission Information
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Keywords
- large language models
- multimodal NLP
- human-in-the-loop NLP
- domain-specific NLP
- responsible and trustworthy NLP
- low-resource and efficient NLP
- evaluation and benchmarking of NLP systems
- generative AI applications
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