What is perplexity used for in AI?
Perplexity measures how well a language model predicts a sample text. It quantifies the average uncertainty or surprise a model has when predicting the next token in a sequence.
A lower perplexity score indicates the model finds the text more predictable and is generally better at prediction tasks. It serves as a key evaluation metric during training and validation to compare model architectures or tune hyperparameters. However, perplexity is calculated based on a specific corpus or test set and doesn't directly measure higher-level language understanding like coherence or factual accuracy.
Its primary application is in evaluating and optimizing language models for text generation tasks. Developers use perplexity scores iteratively to select superior models, fine-tune parameters, and assess the impact of changes. It provides a fast, quantifiable measure to identify models with weaker predictive capabilities before deployment, aiding efficiency in model development.
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