The author explores memorization in large language models through a novel approach named ROME, focusing on disparities between memorized and non-memorized samples using text, probability, and hidden state insights.
Instruction-tuned models exhibit higher levels of memorization than base models when prompted with instruction-based prompts, challenging prior assumptions.