Active Learning with Human-Like Noisy Oracle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it