Sweetening the Dataset : Using Active Learning to Label Unlabelled Datasets
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
Supervised machine learning approaches assume the existence of a large collection of manually labelled examples of the problem under consideration. However, in many cases such a collection does not exist and creating one is time consuming and expensive. This can be a barrier to the use of supervised learning in certain situations, particularly when the doubt as to whether the system will work or not makes the cost of creating a dataset unjustifable. Active learning is a machine learning technique that has been used widely to create classification systems in the absence of large numbers of labelled examples, but that can also be used to create such collections. This paper will describe a system that uses active learning to label large collections of unlabelled data. We will show that the system can create an accurately labelled dataset aproximately 10 times the size of the set of examples manually labelled by an expert. The experiments described are based on recipe data from the 1st Computer Cooking Contest to be held at ECCBR'08 and focus on identifying those recipes in the set that are desserts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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