Bibliographic record
Abstract
When active learning is applied to real-world applications, human experts usually act as oracles to provide labels. However, human make mistakes, thus noise might be introduced during the learning process. Most previous studies simplify the problem by assuming uniformly-distributed noise over the sample space. Such assumption, however, might fail to precisely reflect the human experts' behaviour in real-world situations. In this paper, we therefore study active learning with such human-like oracles, by making a more realistic assumption that the noise is example-dependent (i.e., non-uniformly distributed over the sample space). More specifically, when the human-like oracle is highly confident in labelling examples, it is naturally less likely to provide incorrect answers, whereas when such confidence is low, the noise would be more likely to be introduced. Based on the analysis of such human-like oracle, we propose a generic yet simple active learning algorithm to simultaneously explore the unlabelled data and exploit the labelled data. Empirical study on both synthetic and real-world data sets verifies the superiority of the proposed algorithm, compared with the traditional uncertainty sampling.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".