Concept Drift in Large Language Models

Dr. Ketan Sanjay Desale is a distinguished researcher and educator in the field of computer science, with a specialized focus on artificial intelligence, machine learning, and concept drift detection. Holding a Ph.D. in Computer Engineering, he has built a reputation for his impactful research and commitment to advancing the discipline through both academic and practical applications. Currently serving as an Assistant Professor at Department of Computer Engineering of Pimpri Chinchwad College of Engineering (PCCOE), Pune, India. His contributions to the field are evidenced by his impressive record of publications in esteemed international journals, many of which are indexed in Scopus. Dr. Desale is an active participant in the academic community, regularly presenting his findings at national and international conferences. Dr. Ketan Desale is also the Digital Marketing Head at Pimpri Chinchwad Education Trust (PCET) and serves as the Associate Dean of Management Information Systems (MIS) at Pimpri Chinchwad College of Engineering (PCCOE). In these roles, he leverages his expertise to enhance the institution's digital presence and streamline management information systems, ensuring that both faculty and students benefit from efficient, data-driven decision-making processes. His leadership in these areas reflects his commitment to integrating technology into educational management, ultimately fostering a more innovative and responsive academic environment. Dr. Ketan Desale's academic influence is complemented by his online presence, where he actively shares his research and insights. His profiles on platforms such as Google Scholar, Scopus, and Web of Science highlight his contributions to the scientific community. To learn more about his work, you can visit his personal website or connect with him on LinkedIn.

1. Introduction 2. Concept Drift Fundamentals 3. Large Language Models 4. Concept Drift and Large Language Models 5. Detecting Concept Drift in Language Models 6. Adapting Language Models 7. Natural Language Processing 8. Limitations and Challenges 9. Conclusion & Future Directions