Detecting Concept Drift in Language Models
Ketan Sanjay Desale · 2025
This chapter concentrates on the concept drift, which is detection in language models, and becomes a critical task for the performance of maintenance accuracy and relevance. The overview of methods and techniques for identifying changes in the data distribution from which models are built is covered in the discussion. Traditional statistical techniques and modern algorithms along with their strengths and shortcomings are discussed. Moreover, the chapter provides practical considerations to adapt these methods of detection into real application, in the choosing of tools and the implementation of continuous monitoring. From this viewpoint, understanding nuances in concept drift detection brings enough adaptability to language models in their responsiveness to evolving linguistic patterns and user needs.