Refresh Strategies in Continuous Active Learning
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
High recall information retrieval is crucial to tasks such as electronic discovery and systematic review. Continuous Active Learning (CAL) is a technique where a human assessor works in loop with a machine learning model; the model presents a set of documents likely to be relevant and the assessor provides relevance feedback. Our focus in this thesis is on one particular aspect of CAL: refreshing, which is a crucial and recurring event in the CAL process. During a refresh, the machine learning model is trained with the relevance judgments and a new list of likely-to-be-relevant documents is produced for the assessor to judge. It is also computationally the most expensive step in CAL. In this thesis, we investigate the effects of the default and alternative refresh strategies on the effectiveness and efficiency of CAL. We find that more frequent refreshes can significantly reduce the human effort required to achieve certain recall. For moderately sized datasets, the high computation cost of frequent refreshes can be reduced through a careful implementation. For dealing with resource constraints and large datasets, we propose alternative refresh strategies which provide the benefits of frequent refreshes at a lower computation cost. In this thesis, we also discuss the design of a modern implementation of the CAL algorithm which is efficient and extensible. Our implementation can be used as a research tool as well as for practical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 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 it