Large Language Models (LLMs) excel at solving many classification problems, achieving 70-90% precision with effective prompt engineering. However, challenges arise when the number of classes increases, as LLM performance often declines under constraints like limited training data per class, expensive retraining processes, and the need for rapid inference. The R.E.D algorithm is designed to overcome these limitations, providing a solution for consistent classification accuracy while managing large input classes, thus addressing a significant pain point in applied machine learning.
The R.E.D algorithm effectively addresses the limitations of LLMs in text classification for extensive input classes and improving performance under constrained conditions.
With LLMs achieving 70-90% precision, the need arises for robust classifiers that balance low training data per class and the ability to accommodate new classes.
LLMs like GPT excel in classifying a limited number of categories, but costs and efficiency become prohibitive as the number of classes increases.
Traditional supervised learning struggles under constraints typical in applied ML, making consistent high performance a challenge when managing numerous classes.
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