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No Oops, You Won’t Do It Again: Mechanisms for Self-correction in Crowdsourcing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add a second self-correction stage (workers may revise answers after seeing a reference) | crowdsourced training labels for SVM (linear kernel) · UCI digits and Boston housing, p=0.6, q=0.15 vs 5-9 workers without self-correction | classification error | Helped |
| Add second self-correction stage with 5 workers vs 7 no-self-correction workers, varying first-stage reliability p | crowdsourced training labels for SVM (linear kernel) · UCI digits and Boston housing, reliability p from 0.55 to 0.85 | classification accuracy | Helped |
| Add second self-correction stage with marginal improvement q as low as about 0.1 | crowdsourced training labels for SVM (linear kernel) · p=0.6, q varied 0.05 to 0.4, 5 SC workers vs 7 NSC workers | classification accuracy | Helped |
| Impose no-free-lunch axiom and use the unique mechanism of Algorithm 1 with slack xi_min | payment mechanism for two-stage self-correction · theoretical (incentive compatibility) | incentive compatibility | Helped |
Closest papers by meaning
- Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing (ICML 2023 · similarity 0.89)
- Learning from Crowds by Modeling Common Confusions (AAAI 2021 · similarity 0.86)
- Evaluating LLM-contaminated Crowdsourcing Data Without Ground Truth (NeurIPS 2025 · similarity 0.86)
- Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing (NeurIPS 2016 · similarity 0.86)
- Coupled Confusion Correction: Learning from Crowds with Sparse Annotations (AAAI 2024 · similarity 0.85)
- Efficient Online Crowdsourcing with Complex Annotations (AAAI 2024 · similarity 0.83)
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