Efficient Training Data Reduction Using High-Fidelity Labels and Human Expertise
By
badmonster
Right out the toaster. Reliable, with some real depth.
Summary
The article describes a process for achieving significant training data reduction by using a zero- or few-shot initial model (LLM-0) to label data, followed by clustering and human expert review to curate the most informative and diverse examples. This method addresses the imbalance and low true positive rate of the initial model by focusing on confusable examples along the decision boundary.
Key quotes
· 4 pulledOur process starts with a zero- or few-shot initial model (LLM-0), which we provide with a prompt describing the content of interest.
The LLM-0 model then labels ads as clickbait or benign and generates a large labeled data set.
To find the most informative examples, we separately cluster examples labeled clickbait and examples labeled benign.
The resulting curated set is both informative (since it contains the most confusable examples along the decision boundary) and diverse.
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