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)
Incoming Non-self Citations Over Time
Authors
- 1. Zhencan Peng (Rutgers University)
- 2. Zhizhi Wang (Rutgers University)
- 3. Dong Deng (Rutgers University)
BibTeX Citation
@inproceedings{peng_sigmod23,
title = {{Near-Duplicate Sequence Search at Scale for Large Language Model Memorization Evaluation}},
author = {Peng, Zhencan and Wang, Zhizhi and Deng, Dong},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3589324},
url = {https://dl.acm.org/doi/10.1145/3589324},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,745 | Near-Duplicate Text Alignment with One Permutation Hashing | 2024 | SIGMOD | 5.5534781e-05 |
| 10,265 | LSHAlign: All-Pair Near-Duplicate Text Alignment via LSH | 2026 | SIGMOD | 5.093636e-05 |
| 10,554 | Near-Duplicate Text Alignment under Weighted Jaccard Similarity | 2026 | VLDB | 5.093636e-05 |
| 10,909 | LLMLog: Advanced Log Template Generation via LLM-driven Multi-Round Annotation | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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