CORAL: Collaborative Automatic Labeling System based on Large Language Models
Summary: CORAL combines LLM-generated coarse labels with noisy-label distillation into small language models, reducing annotation and error-review effort. Statistical error discovery, human corrections, iterative refinement, and visual monitoring enable continual, collaborative label improvement. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhen Zhu (Zhejiang University)
- 2. Yibo Wang (Zhejiang University)
- 3. Shouqing Yang (Zhejiang University)
- 4. Lin Long (Zhejiang University)
- 5. Runze Wu (NetEase, Inc.)
- 6. Xiu Tang (Zhejiang University)
- 7. Junbo Zhao (Zhejiang University)
- 8. Haobo Wang (Zhejiang University)
BibTeX Citation
@article{zhu_vldb24,
title = {{CORAL: Collaborative Automatic Labeling System based on Large Language Models}},
author = {Zhu, Zhen and Wang, Yibo and Yang, Shouqing and Long, Lin and Wu, Runze and Tang, Xiu and Zhao, Junbo and Wang, Haobo},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4401--4404},
doi = {10.14778/3685800.3685885},
url = {https://doi.org/10.14778/3685800.3685885},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,303 | LLMLog: Advanced Log Template Generation via LLM-driven Multi-Round Annotation | 2025 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,120 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.00011946047 |
| 3,307 | Cleaning Crowdsourced Labels Using Oracles for Statistical Classification | 2019 | VLDB | 7.4414303e-05 |
| 4,939 | Deep Indexed Active Learning for Matching Heterogeneous Entity Representations | 2022 | VLDB | 6.3458427e-05 |
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