Bibliographic record
Abstract
Labeling datasets is one of the most expensive bottlenecks in data preprocessing tasks in machine learning. Therefore, organizations, in many domains, are applying weak supervision to produce noisy labels. However, since weak supervision relies on cheaper sources, the quality of the generated labels is problematic. Therefore, in this article, we present Asterisk , an end-to-end framework to generate high-quality, large-scale labeled datasets. The system, first, automatically generates heuristics to assign initial labels. Then, the framework applies a novel data-driven active learning process to enhance the labeling quality. We present an algorithm that learns the selection policy by accommodating the modeled accuracies of the heuristics, along with the outcome of the generative model. Finally, the system employs the output of the active learning process to enhance the quality of the labels. To evaluate the proposed system, we report its performance against four state-of-the-art techniques. In collaboration with our industrial partner, IBM, we test the framework within a wide range of real-world applications. The experiments include 10 datasets of varying sizes with a maximum size of 11 million records. The results illustrate the effectiveness of the framework in producing high-quality labels and achieving high classification accuracy with minimal annotation efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.514 | 0.496 |
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 source (direct Gemma or distilled Codex), 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".