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Near-Duplicate Sequence Search at Scale for Large Language Model Memorization Evaluation

Summary: Proposes scalable near-duplicate sequence search to measure LLM memorization in trillion-token corpora. The approach groups min-hash values for all sequences with at least t tokens in linear time, uses inverted indexes and prefix filtering, and proves a bound 2^{(n+1)/(t+1)}−1, with real-world validation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6682
Venue
SIGMOD
Year
2023
Pagerank
4.2667743e-05
Overall Rank
9,876 | 31.30%
DOI
10.1145/3589324

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