Advances in Algorithms for Scalable and Efficient Large Language Models
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 26
Special Issue Editor
Special Issue Information
Dear Colleagues,
Large Language Models (LLMs) have become a transformative force in artificial intelligence, driving advances in natural language understanding, generation, and reasoning across diverse applications. The unprecedented scale and complexity of LLMs present significant algorithmic challenges that require innovative solutions for efficient training, inference, and deployment. This Special Issue focuses on the design, analysis, and implementation of novel algorithms that address these challenges and enable scalable, interpretable, and responsible large-scale language modeling.
We invite original contributions on core algorithmic problems such as optimization techniques for massive model training, advanced and efficient transformer architectures (e.g., sparse or linear attention), and parallel and distributed algorithms that harness heterogeneous hardware environments. Research on model compression algorithms, including pruning, quantization, and knowledge distillation, to accelerate inference while preserving accuracy is also encouraged.
Submissions exploring adaptive algorithms for fine-tuning, prompt engineering, and in-context learning, as well as algorithms enhancing robustness, fairness, and explainability, are welcome. Additionally, privacy-preserving and federated learning algorithms for secure and decentralized LLM training and deployment fall within the scope.
This Special Issue seeks to foster interdisciplinary collaboration between algorithm designers and AI practitioners, emphasizing rigorous algorithmic analysis, empirical validation, and practical implementations. By advancing the algorithmic foundations of LLMs, this collection aims to support the development of scalable, efficient, and ethically aligned AI systems.
Dr. Ishaani Priyadarshini
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 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. Algorithms is an international peer-reviewed open access monthly 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 1800 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
- optimization methods
- scalable algorithms
- transformer architectures
- model compression
- distributed computing
- federated learning
- explainability
- fairness
- robustness
- large language models
- AI ethics
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