The Case For Language Model Approximated LIKE Predicate
Summary: SMILE reframes wildcard LIKE as neural pattern decoding: a compact column-local language model translates complex LIKE predicates into small candidate sets, then verifies via hash lookups. Yields asymptotic, dataset-size-invariant evaluation, robust to drift, outperforming trigram/B+-tree indexes by large margins. (summarized by gpt-5.4-mini on Apr 11 2026)
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Authors
- 1. Yingze Li (Harbin Engineering University)
- 2. Dong Wang (Harbin Engineering University)
- 3. Zixuan Wang (Harbin Engineering University)
- 4. Yingli Zhou (Chinese University of Hong Kong)
- 5. Yu Yan (Harbin Engineering University)
- 6. Jian Geng (Harbin Engineering University)
- 7. Xinyue Wang (Harbin Engineering University)
- 8. Ziqing Zeng (Harbin Engineering University)
- 9. Hongzhi Wang (Harbin Engineering University)
BibTeX Citation
@inproceedings{li_sigmod26,
title = {{The Case For Language Model Approximated LIKE Predicate}},
author = {Li, Yingze and Wang, Dong and Wang, Zixuan and Zhou, Yingli and Yan, Yu and Geng, Jian and Wang, Xinyue and Zeng, Ziqing and Wang, Hongzhi},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786703},
url = {https://dl.acm.org/doi/10.1145/3786703},
year = {2026}
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