DBScholar

Back to papers

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
6744
Venue
SIGMOD
Year
2023
Pagerank
5.1757914e-05
Overall Rank
10,024 | 31.23%
DOI
10.1145/3589324

Incoming Non-self Citations Over Time

Authors

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.

Previous Page 1 / 1 Next

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.

Previous Page 1 / 1 Next

Semantically Similar Papers