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What is perplexity, and how does it relate to LLM performance?
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Answer / Sakshi Rastogi
Perplexity in the context of Large Language Models (LLMs) measures how well the model predicts a sequence of words. A lower perplexity score indicates that the model has a better understanding of the input data, while a higher perplexity score suggests that the model is less certain about its predictions. During training, lowering the model's perplexity can help improve its performance on downstream tasks.
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