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ScaleDoc: Scaling LLM-based Predicates over Large Document Collections

Summary: ScaleDoc decouples LLM predicate execution: offline document representations enable query-time proxy models to filter most documents. Contrastive scoring and adaptive cascades preserve accuracy, yielding 2× speedups and up to 85% fewer LLM calls. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
7479
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,286 | 29.43%
DOI
10.1145/3802106

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BibTeX Citation

@inproceedings{zhang_sigmod26,
        title = {{ScaleDoc: Scaling LLM-based Predicates over Large Document Collections}},
        author = {Zhang, Hengrui and Hui, Yulong and Liu, Yihao and Zhang, Huanchen},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802106},
        url = {https://dl.acm.org/doi/10.1145/3802106},
        year = {2026}
}

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