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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 1252

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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 193
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
Viewed by 489
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 324
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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