Target Model

Target model research focuses on improving the efficiency, robustness, and security of machine learning models, particularly large language models (LLMs). Current efforts concentrate on optimizing model initialization and training through techniques like weight template adaptation and unit scaling, as well as enhancing model security by developing robust defense mechanisms against attacks such as model extraction and membership inference. These advancements are crucial for deploying reliable and secure LLMs in various applications, while simultaneously addressing challenges related to computational cost and data privacy. The field is actively exploring improved training/testing methodologies to ensure the generalizability and reliability of performance evaluations.