Estimating near-verbatim extraction risk in language models with decoding-constrained beam search

A. Feder Cooper; Mark A. Lemley; Christopher De Sa; Lea Duesterwald; Allison Casasola; Jamie Hayes; Katherine Lee; Daniel E. Ho; Percy Liang

arXiv · 2603.24917

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search

A. Feder Cooper·Mark A. Lemley·Christopher De Sa·Lea Duesterwald·Allison Casasola·Jamie Hayes·Katherine Lee·Daniel E. Ho·Percy Liang

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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.

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A. Feder Cooper, Mark A. Lemley, Christopher De Sa, Lea Duesterwald, Allison Casasola, Jamie Hayes, Katherine Lee, Daniel E. Ho, Percy Liang. 2026-03-26. Estimating near-verbatim extraction risk in language models with decoding-constrained beam search. https://arxiv.org/abs/2603.24917

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