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Found 2 papers, 2 papers with code
Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs
In this paper, we introduce a black-box prompt optimization method that uses an attacker LLM agent to uncover higher levels of memorization in a victim agent, compared to what is revealed by prompting the target model with the training data directly, which is the dominant approach of quantifying memorization in LLMs.
Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language Models
Large Language models (LLMs) are trained on vast amounts of data, including sensitive information that poses a risk to personal privacy if exposed.
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