Perplexity is a metric that indicates how well a language model can predict the next word in a text.

What does a high perplexity score mean?

It means the model is uncertain or does not understand the text well.

Why is perplexity important in AI?

Because it allows the performance of language models to be measured and compared objectively.

Perplexity - Perplexity – Measure of Predictive Uncertainty in Language Models

Perplexity is a metric from artificial intelligence, particularly language modeling, that measures how well a language model (e.g. GPT) can predict the next word in a sequence. Technically, it is the exponential of the average negative log-likelihood of the predicted probabilities for a given dataset. The lower the perplexity, the better the model understands the context. A high perplexity indicates uncertainty or a poor fit. In practice, perplexity is widely used to evaluate and compare the performance of different language models.

Metric for evaluating language models
Lower values indicate better predictive performance
Measures the model’s uncertainty
Used in AI research and model training
Can be applied to various text datasets