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Found 3 papers, 1 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.
Finding a Needle in the Adversarial Haystack: A Targeted Paraphrasing Approach For Uncovering Edge Cases with Minimal Distribution Distortion
TPRL leverages FLAN T5, a language model, as a generator and employs a self learned policy using a proximal policy gradient to generate the adversarial examples automatically.
Mitigating Approximate Memorization in Language Models via Dissimilarity Learned Policy
However, these methods rely on explicit and implicit assumptions about the structure of the data to be protected, which often results in an incomplete solution to the problem.
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