Estimating near-verbatim extraction risk in language models with decoding-constrained beam search
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, whi...
| Authors | A. Feder Cooper, Mark A. Lemley, Christopher De Sa et al. |
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| Primary category | cs.CL |
| Published | 26 March 2026 |
| Last updated at source | 31 July 2026 |
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