Perplexity is a measure of how well a probability distribution or statistical model predicts a sample. It is defined as the exponential of the negative log-likelihood of the sample under the model.
In other words, perplexity measures how surprised a model is by the data it sees. A lower perplexity indicates that the model is better able to predict the data, while a higher perplexity indicates that the model is more surprised by the data.
Perplexity is often used to evaluate language models, which are statistical models that can generate text. A lower perplexity indicates that the language model is better able to generate realistic text, while a higher perplexity indicates that the language model is more likely to generate unexpected or unnatural text.
Perplexity is also used in other fields, such as information theory, machine learning, and statistics. In general, perplexity is a useful measure for evaluating the performance of any model that makes predictions about data.
Why is perplexity rising on Google Trends PT?
There are a few possible reasons why perplexity is rising on Google Trends PT. One possibility is that people are becoming more interested in language models and other machine learning models that use perplexity as a metric. Another possibility is that people are becoming more aware of the importance of perplexity in evaluating the performance of these models.
The AI has provided us with the news.
I’ve asked Google Gemini the following question, and here’s its response.
Please search for “perplexity” which is rapidly rising on Google Trends PT and explain in detail. Answers should be in English.
63