Journal Menu

Special Issues

Table of Content

Applied NLP with Large Language Models: AI Applications Across Domains

Submission Deadline: 30 November 2026 View: 1656 Submit to Special Issue

Guest Editor(s)

Prof. Dr. Hsien-Tsung Chang

Email: smallpig@cgu.edu.tw

Affiliation: Bachelor Program in Artificial Intelligence, Chang Gung University, Tao-Yuan, 333, Taiwan

Homepage:

Research Interests: artificial intelligence, natural language processing, affective computing, LLM applications, information retrieval, search engine

图片1.png

Dr. Ammar Amjad

Email: ammar@nycu.edu.tw

Affiliation: Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, 112304, Taiwan

Homepage:

Research Interests: speech processing, natural language processing, deep learning, affective computing, and signal processing for human–computer interaction

Prof. Dr. Lal Khan

Email: lalkhan@gachon.ac.kr

Affiliation: Department of AI and SW, Gachon University, Seongnam, 13120, Repuiblic of Korea

Homepage: https://sites.google.com/view/hcilab/members

Research Interests: AI, NLP, sentiment anaclasis, LLM

Summary

Applied NLP with Large Language Models explores how advanced natural language processing techniques, powered by large-scale pretrained models, can be applied to diverse domains. It covers computational solutions for understanding, generating, and reasoning with natural language, enabling applications such as intelligent information retrieval, text summarization, machine translation, knowledge extraction, decision support, multimodal integration, and human-AI collaboration.

Research areas include prompt engineering, retrieval-augmented generation, domain-specific fine-tuning, interpretability, ethical and trustworthy AI, multilingual processing, and cross-disciplinary applications of LLMs. This Special Issue emphasizes both theoretical advancements in applied NLP with LLMs and practical case studies that demonstrate their impact across industries and sciences.

Potential application areas span engineering, healthcare, education, business, law, and the social sciences, including medical decision support, automated report generation, educational technology, legal document analysis, business intelligence, smart governance, and more.

This Special Issue aims to gather the latest research results and solutions in applied NLP with LLMs. Both methodological and application-driven studies are welcome, particularly those with technical depth or emerging cross-domain applications.

· Prompt Engineering and Optimization
· Retrieval-Augmented Generation (RAG)
· Domain-Specific Fine-Tuning of LLMs
· Multilingual NLP with LLMs
· Interpretability and Explainability of LLMs
· Ethical, Responsible, and Trustworthy NLP Systems
· Human-AI Collaboration in Text Understanding and Generation
· Knowledge Graph Integration with LLMs
· LLM Applications
· Multimodal Applications with LLMs
· Cross-Domain Case Studies

Keywords

large language models, natural language processing, prompt engineering, retrieval-augmented generation, domain adaptation, human-AI collaboration, multimodal AI.

Published Papers

Show export options

Open Access

ARTICLE

Can Causal Circuits Enable Efficient Spatial Reasoning for Geoinformatics in Large Language Models?
Sahil Tripathi, Manaswi Kulahara, Abdul Khader Jilani Saudagar, Hatoon S. AlSagri
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083755
（This article belongs to the Special Issue: Applied NLP with Large Language Models: AI Applications Across Domains)
Abstract Spatial reasoning, defined as the ability to infer and compose relations among entities—is fundamental to geoinformatics applications such as spatial querying and map understanding. However, existing works such as Bidirectional Encoder Representations from Transformers (BERT)-based spatial Question Answering (QA) models and neuro-symbolic models rely on dataset-specific patterns, leading to shortcut learning, where reliance on superficial lexical cues rather than true relational understanding. Recent Large Language Models (LLMs)-based works, including fine-tuning and Chain-of-Thought (CoT) prompting, partially alleviate shortcut learning but remain limited by non-causal reasoning, where predictions depend on spurious correlations rather than stable relational structure. To address these… More >

View
108

Download
24
Open Access

ARTICLE

AAC-HABSA: An Adaptive Aspect Conditioning Framework for Interpretable and Robust Aspect-Based Sentiment Analysis
Mahander Kumar, Lal Khan, Mohammad Zubair Khan, Ibrahim Aljubayri
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.081699
（This article belongs to the Special Issue: Applied NLP with Large Language Models: AI Applications Across Domains)
Abstract Aspect-Based Sentiment Analysis (ABSA) is a fundamental Natural Language Processing (NLP) task that aims to determine fine-grained sentiment polarity toward specific aspects mentioned in text. With the emergence of Large Language Models (LLMs) and transformer-based architectures, significant improvements have been achieved in contextual representation learning for sentiment analysis. However, existing LLM-inspired and transformer-based ABSA frameworks often suffer from inadequate aspect-context alignment, redundant feature integration, limited interpretability, and insufficient coordination between contextual and sequential modeling components. To address these challenges, this paper proposes two hybrid architectures, namely HABSA and AAC-HABSA, centered on a novel Adaptive Aspect… More >
Graphic Abstract

View
205

Download
53

Like
1
Open Access

ARTICLE

LLM-Based Enhanced Clustering for Low-Resource Language: An Empirical Study
Talha Farooq Khan, Majid Hussain, Muhammad Arslan, Muhammad Saeed, Lal Khan, Hsien-Tsung Chang
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3883-3911, 2025, DOI:10.32604/cmes.2025.073021
（This article belongs to the Special Issue: Applied NLP with Large Language Models: AI Applications Across Domains)
Abstract Text clustering is an important task because of its vital role in NLP-related tasks. However, existing research on clustering is mainly based on the English language, with limited work on low-resource languages, such as Urdu. Low-resource language text clustering has many drawbacks in the form of limited annotated collections and strong linguistic diversity. The primary aim of this paper is twofold: (1) By introducing a clustering dataset named UNC-2025 comprises 100k Urdu news documents, and (2) a detailed empirical standard of Large Language Model (LLM) improved clustering methods for Urdu text. We explicitly evaluate the… More >

View
1348

Download
935

Applied NLP with Large Language Models: AI Applications Across Domains

Guest Editor(s)

Summary

Keywords

Published Papers

View

108

Download

24

View

205

Download

53

Like

1

View

1348

Download

935

Further Information

Guidelines

Follow Us

Join Us

Contact Us

WhatsApp:

Share Link