👉 Attending math refers to the ability of a machine learning model, particularly in sequence-to-sequence tasks like question answering or text summarization, to selectively focus on relevant parts of the input sequence while generating an output. This is achieved through a mechanism that assigns different weights to various parts of the input based on their importance for the current task, allowing the model to ignore less relevant information. In simpler terms, when a model "attends" to a math problem, it prioritizes key mathematical concepts and operations over less pertinent details, enabling it to produce more accurate and contextually appropriate responses. This selective focus is crucial for effectively handling complex mathematical queries and generating coherent, mathematically sound answers.
