<ul><li>Text classification can be challenging, especially with a large number of input classes and limited training data.</li><li>The R.E.D. algorithm addresses the problem of semi-supervised learning in text classification.</li><li>R.E.D. utilizes the concept of Recursive Expert Delegation to improve text classification performance.</li><li>The approach involves active learning and human validation to enhance classifier outcomes.</li><li>R.E.D. simplifies the classification task by forming subsets of training labels for better classifier efficiency.</li><li>The algorithm focuses on pre-emptive classification and verification of samples to ensure accuracy.</li><li>Oversampling on noise is used as a measure to prevent misclassifications in the classifier.</li><li>Uncertainty sampling and information gain principles are employed to evaluate the classifier's performance.</li><li>R.E.D. leverages an LLM as a human validator, mimicking the Active Labelling process.</li><li>The algorithm has shown promising results in scaling up to 1,000 classes while maintaining high accuracy levels.</li></ul>

R.E.D.: Scaling Text Classification with Expert Delegation

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