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Innovations in NLP and Large Language Models: Shaping the Future...
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Innovations in NLP and Large Language Models: Shaping the Future of AI

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 1082

Special Issue Editors

Dr. Konstantinos Roumeliotis

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Guest Editor
Department of Digital Systems, University of the Peloponnese, 23100 Sparta, Greece
Interests: applied AI; natural language processing; large language models; software engineering
Prof. Dr. Nikolaos Tselikas

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Guest Editor
Department of Informatics and Telecommunications, University of Peloponnese, Akadimaikou G. K. Vlachou Street, 22131 Tripoli, Greece
Interests: mobile services and applications; network services; open APIs and software/middleware technologies; software performance optimization; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural Language Processing (NLP) and Large Language Models (LLMs) are at the forefront of innovations in artificial intelligence (AI), driving significant advancements across industries. With the rapid evolution of machine learning techniques, LLMs have revolutionized how machines understand, generate, and interact with human language, enabling more intuitive conversational agents, automated content generation, and enhanced language comprehension. This Special Issue aim to explore the latest breakthroughs in NLP and LLMs, focusing on how these technologies are shaping the future of AI. Contributions will cover a range of topics, from novel NLP techniques and architectures to the real-world application of LLMs, showcasing their transformative potential in areas such as business, healthcare, education, human–computer interaction, business analytics, business intelligence (BI), decision support systems (DSSs), and beyond. By compiling cutting-edge research, this Special Issue aims to offer new insights and inspire further innovation in the field.

In this Special Issue, original research articles and reviews are welcome. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Natural Language Processing (NLP)
  • Large Language Models (LLMs)
  • Artificial Intelligence (AI)
  • Machine Learning
  • Deep Learning
  • Applied AI
  • Conversational AI
  • Human–Computer Interaction
  • NLP Applications
  • LLM Applications

Dr. Konstantinos Roumeliotis
Prof. Dr. Nikolaos Tselikas
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • Natural Language Processing (NLP)
  • Large Language Models (LLMs)
  • Artificial Intelligence (AI)
  • machine learning
  • deep learning
  • applied AI
  • conversational AI
  • human–computer interaction
  • NLP applications
  • LLM applications

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Published Papers (1 paper)

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Research

18 pages, 2055 KiB  
Article
Think Before You Classify: The Rise of Reasoning Large Language Models for Consumer Complaint Detection and Classification
by Konstantinos I. Roumeliotis, Nikolaos D. Tselikas and Dimitrios K. Nasiopoulos
Electronics 2025, 14(6), 1070; https://doi.org/10.3390/electronics14061070 - 7 Mar 2025
Viewed by 882
Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing (NLP) tasks, but their effectiveness in real-world consumer complaint classification without fine-tuning remains uncertain. Zero-shot classification offers a promising solution by enabling models to categorize consumer complaints without prior exposure [...] Read more.
Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing (NLP) tasks, but their effectiveness in real-world consumer complaint classification without fine-tuning remains uncertain. Zero-shot classification offers a promising solution by enabling models to categorize consumer complaints without prior exposure to labeled training data, making it valuable for handling emerging issues and dynamic complaint categories in finance. However, this task is particularly challenging, as financial complaint categories often overlap, requiring a deep understanding of nuanced language. In this study, we evaluate the zero-shot classification performance of leading LLMs and reasoning models, totaling 14 models. Specifically, we assess DeepSeek-V3, Gemini-2.0-Flash, Gemini-1.5-Pro, Anthropic’s Claude 3.5 and 3.7 Sonnet, Claude 3.5 Haiku, and OpenAI’s GPT-4o, GPT-4.5, and GPT-4o Mini, alongside reasoning models such as DeepSeek-R1, o1, and o3. Unlike traditional LLMs, reasoning models are specifically trained with reinforcement learning to exhibit advanced inferential capabilities, structured decision-making, and complex reasoning, making their application to text classification a groundbreaking advancement. The models were tasked with classifying consumer complaints submitted to the Consumer Financial Protection Bureau (CFPB) into five predefined financial classes based solely on complaint text. Performance was measured using accuracy, precision, recall, F1-score, and heatmaps to identify classification patterns. The findings highlight the strengths and limitations of both standard LLMs and reasoning models in financial text processing, providing valuable insights into their practical applications. By integrating reasoning models into classification workflows, organizations may enhance complaint resolution automation and improve customer service efficiency, marking a significant step forward in AI-driven financial text analysis. Full article
(This article belongs to the Special Issue Innovations in NLP and Large Language Models: Shaping the Future of AI)
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