
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.


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Memorization-Insights-from-Large-Language-Models-ROME-Study


Memorization Insights from Large Language Models: ROME Study


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Instruction-tuned models exhibit higher levels of memorization than base models when prompted with instruction-based prompts, challenging prior assumptions.


uncovering-memorization-in-instruction-tuned-llms


Uncovering Memorization in Instruction-Tuned LLMs
