Asad Ullah Khan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Asad Ullah Khan has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
Aiman Siddiqi
University of Engineering and Technology Lahore
1 shared publication- Ajmal Mian
The University of Western Australia
1 shared publication - Gulraiz Khan
Al-Khawarizmi Institute of Computer Sciences, University of Engineering and Technology, Lahore, Pakistan
1 shared publication - Humza Naveed
University of Engineering and Technology Lahore
1 shared publication - Humza Naveed
The University of Sydney, Australia
1 shared publication - Muhammad Saqib
National Collection and Marine Infrastructure, CSIRO, Canberra, Australia
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
- 18 August 2025
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Intelligent Systems and Technology
- Vol. 16 (5) , 1-72
A Comprehensive Overview of Large Language Models
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multimodal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to provide not only a systematic survey but also a quick, comprehensive reference for the researchers and practitioners to draw insights from extensive, informative summaries of the existing works to advance the LLM research.
- 18 August 2025
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Intelligent Systems and Technology
- Vol. 16 (5) , 1-72
Top Cited PapersOpen AccessA Comprehensive Overview of Large Language Models
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multimodal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to provide not only a systematic survey but also a quick, comprehensive reference for the researchers and practitioners to draw insights from extensive, informative summaries of the existing works to advance the LLM research.
- 24 September 2018
- Published by Springer Nature in International Journal of Machine Learning and Cybernetics
- Vol. 10 (9) , 2329-2340
Human activity recognition using mixture of heterogeneous features and sequential minimal optimization
- 24 September 2018
- Published by Springer Nature in International Journal of Machine Learning and Cybernetics
- Vol. 10 (9) , 2329-2340
Human activity recognition using mixture of heterogeneous features and sequential minimal optimization