Learning from Crowds with Annotation Reliability
Bibliographic record
Abstract
Crowdsourcing provides a practical approach for obtaining annotated data to train supervised learning models. However, since the crowd annotators may have different expertise domain and cannot always guarantee the high-quality annotations, learning from crowds generally suffers from the problem of unreliable results of introducing some noises, which makes it hard to achieve satisfying performance. In this work, we investigate the reliability of annotations to improve learning from crowds. Specifically, we first project annotator and data instance to factor vectors and model the complex interaction between annotator expertise and instance difficulty to predict annotation reliability. The learned reliability can be used to evaluate the quality of crowdsourced data directly. Then, we construct a new annotation, namely soft annotation, which serves as the gold label during the training. To recognize the different strengths of annotators, we model each annotator's confusion in an end-to-end manner. Extensive experimental results on three real-world datasets demonstrate the effectiveness of our method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".