Measuring memorization through probabilistic discoverable extraction
Large language models (LLMs) are susceptible to memorizing training data,
raising concerns due to the potential extraction of sensitive information.
Current methods to measure memorization rates of LLMs, primarily discoverable
extraction (Carlini et al., 2022), rely on single-sequence greedy sampling,
potentially underestimating the true extent of memorization. This paper
introduces a probabilistic relaxation of discoverable extraction that
quantifies the probability of extracting a target sequence within a set of
generated samples, considering various sampling schemes and multiple attempts.
This approach addresses the limitations of reporting memorization rates through
discoverable extraction by accounting for the probabilistic nature of LLMs and
user interaction patterns. Our experiments demonstrate that this probabilistic
measure can reveal cases of higher memorization rates compared to rates found
through discoverable extraction. We further investigate the impact of different
sampling schemes on extractability, providing a more comprehensive and
realistic assessment of LLM memorization and its associated risks. Our
contributions include a new probabilistic memorization definition, empirical
evidence of its effectiveness, and a thorough evaluation across different
models, sizes, sampling schemes, and training data repetitions.
67226a32ea6e0084fc0f029c
Jamie Hayes
67226a32ea6e0084fc0f029d
Marika Swanberg
67226a32ea6e0084fc0f029e
Harsh Chaudhari
67226a32ea6e0084fc0f029f
Itay Yona
67226a32ea6e0084fc0f02a0
Ilia Shumailov
Measuring memorization through probabilistic discoverable extraction
Large language models (LLMs) are susceptible to memorizing training data,
raising concerns due to the potential extraction of sensitive information.
Current methods to measure memorization rates of LLMs, primarily discoverable
extraction (Carlini et al., 2022), rely on single-sequence greedy sampling,
potentially underestimating the true extent of memorization. This paper
introduces a probabilistic relaxation of discoverable extraction that
quantifies the probability of extracting a target sequence within a set of
generated samples, considering various sampling schemes and multiple attempts.
This approach addresses the limitations of reporting memorization rates through
discoverable extraction by accounting for the probabilistic nature of LLMs and
user interaction patterns. Our experiments demonstrate that this probabilistic
measure can reveal cases of higher memorization rates compared to rates found
through discoverable extraction. We further investigate the impact of different
sampling schemes on extractability, providing a more comprehensive and
realistic assessment of LLM memorization and its associated risks. Our
contributions include a new probabilistic memorization definition, empirical
evidence of its effectiveness, and a thorough evaluation across different
models, sizes, sampling schemes, and training data repetitions.
A probabilistic method for measuring memorization in large language models provides a more comprehensive assessment by considering various sampling schemes and multiple attempts compared to deterministic discoverable extraction.
large language models memorization discoverable extraction probabilistic relaxation sampling schemes memorization rates user interaction patterns probabilistic memorization definition empirical evidence