SemDistill: Bootstrapping a Low-Latency, Low-Cost Semantic Table Annotator from Noisy LLM
Summary: SemDistill distills noisy LLM predictions for table annotation into fast, private local models using scarce trusted labels. It corrects global ontological drift and identifies high-confidence hallucinations via reliability-tiered training, outperforming its teacher at 3,800× lower cost and 2,600× lower latency. (summarized by gpt-6-luna on Oct 22 2026)
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Authors
- 1. Yuhang Ge (Zhejiang University)
- 2. Zhangyan Ye (Zhejiang University)
- 3. Yuren Mao (Zhejiang University)
- 4. Yunjun Gao (Zhejiang University)
BibTeX Citation
@inproceedings{ge_sigmod27,
title = {{SemDistill: Bootstrapping a Low-Latency, Low-Cost Semantic Table Annotator from Noisy LLM}},
author = {Ge, Yuhang and Ye, Zhangyan and Mao, Yuren and Gao, Yunjun},
series = {{SIGMOD} '27},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3837124},
url = {https://dl.acm.org/doi/10.1145/3837124},
year = {2027}
}
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