A new probing framework detects moderate parametric memorization signals in tabular in-context learning models under single-task fine-tuning, strongest on low-cardinality tasks, but signals largely disappear under realistic training.
arXiv preprint arXiv:2303.00654 (2023)
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Hashing-based framework adds DP noise to LSH bucket votes to release private probability distributions for datastores with 2.6% average accuracy loss at epsilon=5.
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Probing Memorization of Tabular In-Context Learning
A new probing framework detects moderate parametric memorization signals in tabular in-context learning models under single-task fine-tuning, strongest on low-cardinality tasks, but signals largely disappear under realistic training.