Data Acquisition for Improving Model Confidence
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
In recent years, there has been a growing recognition that high-quality training data is crucial for the performance of machine learning models. This awareness has catalyzed both research endeavors and industrial initiatives dedicated to data acquisition to enhance diverse dimensions of model performance. Among these dimensions, model confidence holds paramount importance; however, it has often been overlooked in prior investigations into data acquisition methodologies. To address this gap, our work focuses on improving the data acquisition process with the goal of enhancing the confidence of Machine Learning models. Specifically, we operate within a practical context where limited samples can be obtained from a large data pool. We employ well-established model confidence metrics as our foundation, and we propose two methodologies, Bulk Acquisition (BA) and Sequential Acquisition (SA), each geared towards identifying the sets of samples that yield the most substantial gains in model confidence. Recognizing the complexity of BA and SA, we introduce two efficient approximate methods, namely kNN-BA and kNN-SA, restricting data acquisition to promising subsets within the data pool. To broaden the applicability of our solutions, we introduce a Distribution-based Acquisition approach that makes minimal assumption regarding the data pool and facilitates the data acquisition across various settings. Through extensive experimentation encompassing diverse datasets, models, and parameter configurations, we demonstrate the efficacy of our proposed methods across a range of tasks. Comparative experiments with alternative applicable baselines underscore the superior performance of our proposed approaches.
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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 | 0.000 |
| 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 | 0.002 |
| Open science | 0.015 | 0.012 |
| 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