New Insights into Natural Language Processing and Large Language Models

Dear Colleagues,

We are pleased to announce the launch of this Special Issue, entitled “New Insights into Natural Language Processing and Large Language Models”, and you are invited to submit your latest research.

Rapid advances in natural language processing (NLP) and large language models (LLMs) have revolutionized a wide range of applications, from machine translation and conversational AI to automated content generation and text-based reasoning. As these models evolve, new challenges and opportunities arise to improve their interpretability, fairness, efficiency, and adaptability. This Special Issue aims to bring together recent research that improves our understanding of LLMs and their implications for NLP.

We welcome the submission of both theoretical and applied research contributions that explore novel architectures, innovative training paradigms, evaluation techniques, and real-world applications of NLP systems. In particular, we encourage submissions that provide empirical insights, introduce new methodologies, or critically examine the ethical and societal implications of LLMs.

Topics of Interest

Relevant topics of interest for this Special Issue include, but are not limited to, the following:

• Advances in LLM architectures and training methodologies;
• Efficient and scalable NLP models for real-world applications;
• Interpretability, fairness, and bias mitigation in LLMs;
• Domain adaptation and task-specific fine-tuning of large models;
• Multimodal and cross-lingual NLP using LLMs;
• Theoretical analyses of emergent behaviors in LLMs;
• Integration of symbolic reasoning and structured knowledge into LLMs;
• Energy-efficient and environmentally sustainable NLP solutions;
• Applications of LLMs in industry, healthcare, finance, legal, and other domains;
• Ethical considerations, misinformation, and regulatory challenges in LLM development.

Relation to the Existing Literature

This Special Issue aims to build upon the growing body of research in NLP and deep learning, addressing recent trends and ongoing challenges. While transformer-based architectures have significantly advanced this field, gaps remain in areas such as model interpretability, computational efficiency, and real-world deployment. The current literature primarily focuses on general-purpose LLMs, whereas this Special Issue seeks to explore specialized adaptations, theoretical insights, and practical constraints. By bringing together a diverse range of contributions, this collection aims to serve as a valuable resource for researchers and practitioners pushing the boundaries of NLP and LLM research. We invite scholars from academia and industry to submit their latest findings and contribute to shaping the future of NLP.

We look forward to receiving your submissions and advancing the discourse on NLP and LLMs together.

Dr. Guang Lu
Dr. Nianlong Gu
Dr. Farhad Nooralahzadeh
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.

• natural language processing (NLP)
• large language models (LLMs)
• deep learning for NLP
• explainability and interpretability in NLP
• multimodal language processing
• ethics and bias in LLMs
• applications of NLP and LLMs in industry