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Electronics
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AI-Powered Natural Language Processing Applications
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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 3640

Special Issue Editor

Dr. Weibin Wu

E-Mail Website
Guest Editor
School of Software Engineering, Sun Yat-sen University, Zhuhai 519082, China
Interests: trustworthy artificial intelligence; large language model; computer vision; intelligent software engineering

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Published Papers (6 papers)

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Research

22 pages, 1557 KB  
Article
A Culturally Aware LLM Framework for Analyzing Social Engineering Tactics in Korean Phishing Messages
by Kiho Lee, Yongjoon Lee, Jaeyeong Jeong, Yong-ha Choi and Dongkyoo Shin
Electronics 2026, 15(10), 2196; https://doi.org/10.3390/electronics15102196 - 20 May 2026
Abstract
Phishing messages have evolved from simple fraud templates into socially engineered texts that exploit anxiety, trust, relational obligation, and culturally embedded norms. In Korean phishing messages, attackers frequently combine institutional authority, family or acquaintance framing, requests for cooperation, and urgency cues to induce [...] Read more.
Phishing messages have evolved from simple fraud templates into socially engineered texts that exploit anxiety, trust, relational obligation, and culturally embedded norms. In Korean phishing messages, attackers frequently combine institutional authority, family or acquaintance framing, requests for cooperation, and urgency cues to induce concrete victim actions such as money transfer, link clicking, phone contact, app installation, or credential submission. However, prior studies have largely emphasized binary phishing detection while offering limited interpretability regarding how such messages mobilize social and cultural persuasion strategies. This study proposes a culturally aware large language model framework for analyzing social engineering tactics in Korean phishing messages. The framework is built on a multidimensional codebook that represents the message text, phishing label, tactic type, relation type, requested action, cultural lever, and evidence span, enabling structured and explainable analysis beyond simple classification. To operationalize this framework, an OpenChat-based model is fine-tuned with QLoRA to generate structured outputs that jointly predict the phishing status and socially relevant attributes, while evidence-span supervision is incorporated to improve grounding and explanation consistency. The evaluation examines not only phishing-detection performance but also attribute-level prediction accuracy, evidence alignment, parsing reliability, and human-rated usefulness and trustworthiness. By integrating the cultural context, relational framing, and evidence-grounded explanation into LLM-based phishing analysis, this study provides an interpretable analytical framework for Korean phishing messages and an evidence-grounded basis for analyst-supportive phishing triage. On the 82-sample authoritative clean hold-out split, Model D produced error-free label predictions and achieved 0.841 exact-match core and 0.886 span-F1. However, because the evaluation used a single 82-sample internal hold-out split and no independent external corpus, these results should be interpreted as feasibility evidence under leakage-controlled conditions rather than as proof of deployment-level robustness or cross-domain generalization. The main contribution of this study is therefore not improved binary detection over strong lexical baselines, but the structured and evidence-grounded representation of Korean phishing persuasion tactics for analyst-supportive triage. Full article
(This article belongs to the Special Issue AI-Powered Natural Language Processing Applications)
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26 pages, 2904 KB  
Article
Cross-Modal Semantic Alignment and Dynamic Routing Enhancement for Inspection and Supervision Scenarios
by Changhua Hu, Jianfeng Liu, Zheng Cheng, Hu Han, Yuetian Huang, Qingguo Shi and Yi Su
Electronics 2026, 15(9), 1846; https://doi.org/10.3390/electronics15091846 - 27 Apr 2026
Viewed by 364
Abstract
Traditional inspection and supervision in power grid operations suffer from heterogeneous multi-source data (text, tables, and images), low policy retrieval efficiency, difficult issue characterization, non-standardized reporting, and weak closed-loop rectification. To address these challenges in Guangdong Power Grid scenarios, this paper proposes CSA-DR, [...] Read more.
Traditional inspection and supervision in power grid operations suffer from heterogeneous multi-source data (text, tables, and images), low policy retrieval efficiency, difficult issue characterization, non-standardized reporting, and weak closed-loop rectification. To address these challenges in Guangdong Power Grid scenarios, this paper proposes CSA-DR, a Cross-modal Semantic Alignment and Dynamic Routing enhancement method. CSA-DR retrieves relevant policy documents, structured tables, and inspection-related images from an external regulatory knowledge base and encodes them via a tri-modal into a unified semantic space, achieving precise cross-modal alignment between inspection descriptions and supporting evidence. A dynamic routing mechanism is introduced to adaptively allocate modality importance according to task requirements, significantly improving key information extraction, violation detection, and causal analysis. Additionally, the framework integrates an external regulatory knowledge base. For each inspection task, relevant policy documents, structured tables, and evidence images are retrieved from this knowledge base and used as the tri-modal input to the model. This knowledge-grounded design enables cross-modal semantic alignment, evidence traceability, and standardized inspection report generation. Experiments on a real multi-source inspection dataset from Guangdong Power Grid show that CSA-DR consistently outperforms the compared baseline methods and ablation variant across all applicable metrics, with notable improvements in cross-modal MRR and image-to-image Recall@5. Full article
(This article belongs to the Special Issue AI-Powered Natural Language Processing Applications)
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23 pages, 973 KB  
Article
Evaluation of Linguistic Consistency of LLM-Generated Text Personalization Using Natural Language Processing
by Linh Huynh and Danielle S. McNamara
Electronics 2026, 15(6), 1262; https://doi.org/10.3390/electronics15061262 - 18 Mar 2026
Cited by 1 | Viewed by 893
Abstract
This study proposes a Natural Language Processing (NLP)-based evaluation framework to examine the linguistic consistency of large language model (LLM)-generated personalized texts over time. NLP metrics were used to quantify and compare linguistic patterns across repeated generations produced using identical prompts. In Experiment [...] Read more.
This study proposes a Natural Language Processing (NLP)-based evaluation framework to examine the linguistic consistency of large language model (LLM)-generated personalized texts over time. NLP metrics were used to quantify and compare linguistic patterns across repeated generations produced using identical prompts. In Experiment 1, internal reliability was examined across 10 repeated generations from four LLMs (Claude, Llama, Gemini, and ChatGPT), applied to 10 scientific texts tailored for a specific reader profile. Linear mixed-effects models showed no effect of repeated generation on linguistic features (e.g., cohesion, syntactic complexity, lexical sophistication), suggesting short-term consistency across repeatedly generated outputs. Experiment 2 examined linguistic variation across model updates of GPT-4o (October 2024 vs. June 2025) and GPT-4.1 (June 2025). Significant variations were observed across outputs from different model versions. GPT-4o (June 2025) generated more concise but cohesive texts, whereas GPT-4.1 (June 2025) generated outputs that are more academic, lexically sophisticated, and complex in syntax. Given the rapid evolution of LLMs and the lack of standardized methods for tracking output consistency, the current work demonstrates one of the applications of NLP-based evaluation approaches for monitoring meaningful linguistic shifts across model updates over time. Full article
(This article belongs to the Special Issue AI-Powered Natural Language Processing Applications)
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26 pages, 403 KB  
Article
How the Representation of Retrieved Context Affects In-Context Prompting for Commit Message Generation
by Dokyeong An and Geunseok Yang
Electronics 2026, 15(3), 652; https://doi.org/10.3390/electronics15030652 - 2 Feb 2026
Viewed by 304
Abstract
High-quality commit messages are essential software artifacts because they succinctly communicate the intent and scope of code changes, yet large language models (LLMs) often fail to reflect project-specific writing conventions when used in a zero-shot setting without contextual signals. This study investigates not [...] Read more.
High-quality commit messages are essential software artifacts because they succinctly communicate the intent and scope of code changes, yet large language models (LLMs) often fail to reflect project-specific writing conventions when used in a zero-shot setting without contextual signals. This study investigates not whether retrieval helps, but how the same retrieved example, when represented differently in the prompt, quantitatively changes generation outcomes. We implement a retrieve-then-generate framework where the target commit’s diff is used as a query for BM25 (Best Matching 25)-based sparse retrieval over a commit-level database, and the top-1 similar commit is optionally injected as an example context. We compare a no-context condition (K = 0) against a minimal-context condition (K = 1) under three context representations: Diff-only, Message-only, and Diff + Message pair. Using Qwen-7B on 8000 evaluation samples with a fixed prompt skeleton, deterministic decoding, and identical post-processing across conditions, we observe negligible differences at K = 0 (BLEU-4 1.14, ROUGE-L 7.47–7.48, METEOR 4.88–4.91), establishing a stable baseline. At K = 1, the same top-1 retrieved case yields systematically different metric responses depending on how it is represented (Diff-only, Message-only, or Diff + Message), even under an identical prompt skeleton, deterministic decoding, and identical post-processing. This indicates that “context representation” is not a cosmetic formatting choice but a first-class prompt-design variable in retrieval-augmented in-context learning for commit message generation. Accordingly, practitioners should select the representation based on the intended objective (e.g., lexical/style alignment vs. change-intent grounding), rather than assuming a universally optimal format. Full article
(This article belongs to the Special Issue AI-Powered Natural Language Processing Applications)
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23 pages, 2309 KB  
Article
SLTP: A Symbolic Travel-Planning Agent Framework with Decoupled Translation and Heuristic Tree Search
by Debin Tang, Qian Jiang, Jingpu Yang, Jingyu Zhao, Xiaofei Du, Miao Fang and Xiaofei Zhang
Electronics 2026, 15(2), 422; https://doi.org/10.3390/electronics15020422 - 18 Jan 2026
Cited by 1 | Viewed by 860
Abstract
Large language models (LLMs) demonstrate outstanding capability in understanding natural language and show great potential in open-domain travel planning. However, when confronted with multi-constraint itineraries, personalized recommendations, and scenarios requiring rigorous external information validation, pure LLM-based approaches lack rigorous planning ability and fine-grained [...] Read more.
Large language models (LLMs) demonstrate outstanding capability in understanding natural language and show great potential in open-domain travel planning. However, when confronted with multi-constraint itineraries, personalized recommendations, and scenarios requiring rigorous external information validation, pure LLM-based approaches lack rigorous planning ability and fine-grained personalization. To address these gaps, we propose the Symbolic LoRA Travel Planner (SLTP) framework—an agent architecture that combines a two-stage symbol-rule LoRA fine-tuning pipeline with a user multi-option heuristic tree search (MHTS) planner. SLTP decomposes the entire process of transforming natural language into executable code into two specialized, sequential LoRA experts: the first maps natural-language queries to symbolic constraints with high fidelity; the second compiles symbolic constraints into executable Python planning code. After reflective verification, the generated code serves as constraints and heuristic rules for an MHTS planner that preserves diversified top-K candidate itineraries and uses pruning plus heuristic strategies to maintain search-time performance. To overcome the scarcity of high-quality intermediate symbolic data, we adopt a teacher–student distillation approach: a strong teacher model generates high-fidelity symbolic constraints and executable code, which we use as hard targets to distill knowledge into an 8B-parameter Qwen3-8B student model via two-stage LoRA. On the ChinaTravel benchmark, SLTP using an 8B student achieves performance comparable to or surpassing that of other methods built on DeepSeek-V3 or GPT-4o as a backbone. Full article
(This article belongs to the Special Issue AI-Powered Natural Language Processing Applications)
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22 pages, 795 KB  
Article
HIEA: Hierarchical Inference for Entity Alignment with Collaboration of Instruction-Tuned Large Language Models and Small Models
by Xinchen Shi, Zhenyu Han and Bin Li
Electronics 2026, 15(2), 421; https://doi.org/10.3390/electronics15020421 - 18 Jan 2026
Viewed by 718
Abstract
Entity alignment (EA) facilitates knowledge fusion by matching semantically identical entities in distinct knowledge graphs (KGs). Existing embedding-based methods rely solely on intrinsic KG facts and often struggle with long-tail entities due to insufficient information. Recently, large language models (LLMs), empowered by rich [...] Read more.
Entity alignment (EA) facilitates knowledge fusion by matching semantically identical entities in distinct knowledge graphs (KGs). Existing embedding-based methods rely solely on intrinsic KG facts and often struggle with long-tail entities due to insufficient information. Recently, large language models (LLMs), empowered by rich background knowledge and strong reasoning abilities, have shown promise for EA. However, most current LLM-enhanced approaches follow the in-context learning paradigm, requiring multi-round interactions with carefully designed prompts to perform additional auxiliary operations, which leads to substantial computational overhead. Moreover, they fail to fully exploit the complementary strengths of embedding-based small models and LLMs. To address these limitations, we propose HIEA, a novel hierarchical inference framework for entity alignment. By instruction-tuning a generative LLM with a unified and concise prompt and a knowledge adapter, HIEA produces alignment results with a single LLM invocation. Meanwhile, embedding-based small models not only generate candidate entities but also support the LLM through data augmentation and certainty-aware source entity classification, fostering deeper collaboration between small models and LLMs. Extensive experiments on both standard and highly heterogeneous benchmarks demonstrate that HIEA consistently outperforms existing embedding-based and LLM-enhanced methods, achieving absolute Hits@1 improvements of up to 5.6%, while significantly reducing inference cost. Full article
(This article belongs to the Special Issue AI-Powered Natural Language Processing Applications)
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