👉 Exposure math is a fundamental concept in machine learning, particularly in natural language processing (NLP), used to quantify the amount of data a model has seen during training. It's crucial for understanding how well a model can generalize to new, unseen data. Exposure is typically measured in terms of the number of times a word or token appears in the training corpus, often normalized by the total number of tokens to account for varying text lengths. A higher exposure generally implies more robust learning, as the model encounters a wider variety of examples, but it's important to balance this with the risk of overfitting, where the model might memorize specific instances rather than learning general patterns. This concept is often visualized using a histogram of exposure values, helping practitioners gauge the model's training quality and its potential to handle new data effectively.
