Natural Language Processing Based on Neural Networks and Large Language Models

Dear Colleagues,

We are excited to announce the launch of a new Special Issue entitled ‘Natural Language Processing Based on Neural Networks and Large Language Models’ in the journal Electronics. This Special Issue aims to explore the transformative impact of neural networks (NNs) and large language models (LLMs) on the field of natural language processing (NLP), highlighting advancements that are redefining the way machines understand and generate human language.

Natural language processing (NLP) has experienced a revolutionary shift with the advent of neural networks (NNs) and large language models (LLMs). These technologies enable advanced applications such as machine translation, sentiment analysis, and conversational agents. This Special Issue focuses on the latest advancements in NN- and LLM-based NLP, addressing challenges in scalability, efficiency, and ethical use, while showcasing innovative methodologies and real-world applications.

This Special Issue will delve into cutting-edge applications and methodologies leveraging NNs and LLMs for NLP, covering topics such as machine translation, conversational AI, sentiment analysis, text generation, and beyond.

We welcome original research, review articles, and case studies that explore the theoretical foundations, algorithmic innovations, and practical implementations of NNs and LLMs in NLP. Special emphasis will be placed on topics such as improving model efficiency, scalability, interpretability, and ethical concerns in AI development.

This Special Issue seeks to bridge gaps in the existing literature by providing a platform for novel ideas and multidisciplinary approaches to NLP. With rapid advancements in NN and LLM technologies, this collection will offer a timely and comprehensive perspective on their evolving role in solving complex linguistic challenges across diverse domains.

While existing research has extensively explored traditional NLP approaches, the rapid development of NN and LLM technologies introduces new challenges and opportunities. This Special Issue will provide an invaluable supplement to the current literature by achieving the following:

• Showcasing innovative methods that address the limitations of earlier NLP techniques.
• Discussing the practical implications of NN- and LLM-based solutions in real-world applications.
• Addressing ethical and societal considerations that arise with the deployment of large-scale language models.

We invite you to submit your latest research to contribute to this exciting field.

Dr. Krzysztof Wolk
Prof. Dr. Xiangjian He
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 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.