Model Representation

Model representation research focuses on understanding and improving how machine learning models encode information, aiming to enhance efficiency, interpretability, and robustness. Current efforts concentrate on analyzing the geometric properties of model representations learned through next-token prediction, mitigating catastrophic forgetting during fine-tuning, and developing methods to efficiently transfer representations between models and datasets. These advancements are crucial for improving the performance, scalability, and trustworthiness of various machine learning applications, particularly in large language models and computer vision.