Using Perplexity to eliminate known data points

This guide explains how to use perplexity, a metric for evaluating language models, to determine the importance of data points in clusters for training large language models (LLMs). By clustering embeddings and calculating perplexity scores for each cluster, irrelevant training data can be eliminated. The process involves loading a dataset, embedding it using an appropriate model, clustering the data, assigning samples to each cluster, creating sample datasets for fine-tuning LLMs, and filtering out clusters with low average perplexity scores. This method helps reduce the size of the training dataset while maintaining or improving model performance.

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