aiqbench analysis
This research introduces decoding-constrained beam search as an efficient method for estimating near-verbatim extraction risks in large language models, addressing the computational bottlenecks of standard Monte Carlo sampling. By providing deterministic lower bounds at a fraction of the cost, the technique reveals previously hidden patterns regarding memorization and privacy leakage across different model architectures. This advancement is critical for improving safety evaluations and copyright compliance strategies without prohibitive resource expenditure.
Estimating near-verbatim extraction risk in language models with decoding-constrained beam search
arXiv:2603.24917v2 Announce Type: replace Abstract: Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across sequences. Probabilistic extraction -- computing the probability of generating a target suffix given a prefix under a decoding scheme -- addresses this, but is tractable only for verbatim memorization, missing near-verbatim instances that pose similar privacy and copyright risks. Quantifying near-verbatim extraction risk is expensive: the set of near-verbatim suffixes is combinatorially large, and reliable Monte Carlo (MC) estimation can require ~100,000 samples per sequence. To mitigate this cost, we introduce decoding-constrained beam search, which yields deterministic lower bounds on near-verbatim extraction risk at a cost comparable to ~20 MC samples per sequence. Across experiments, our approach surfaces information invisible to verbatim methods: many more extractable sequences, substantially larger per-sequence extraction mass, and patterns in how near-verbatim extraction risk manifests across model sizes and types of text.
Abstract:Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across sequences. Probabilistic extraction -- computing the probability of generating a target suffix given a prefix under a decoding scheme -- addresses this, but is tractable only for verbatim memorization, missing near-verbatim instances that pose similar privacy and copyright risks. Quantifying near-verbatim extraction risk is expensive: the set of near-verbatim suffixes is combinatorially large, and reliable Monte Carlo (MC) estimation can require ~100,000 samples per sequence. To mitigate this cost, we introduce decoding-constrained beam search, which yields deterministic lower bounds on near-verbatim extraction risk at a cost comparable to ~20 MC samples per sequence. Across experiments, our approach surfaces information invisible to verbatim methods: many more extractable sequences, substantially larger per-sequence extraction mass, and patterns in how near-verbatim extraction risk manifests across model sizes and types of text.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2603.24917 [cs.CL]
(or arXiv:2603.24917v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2603.24917 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 242 words
