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)
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Authors
- 1. Hengrui Zhang (Tsinghua University)
- 2. Yulong Hui (Tsinghua University)
- 3. Yihao Liu (Tsinghua University)
- 4. Huanchen Zhang (Shanghai Qi Zhi Institute; Tsinghua University)
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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