Tell about the idea of Longformer and its sparse attention.

Longformer is a transformer architecture designed to handle very long sequences, where the traditional attention mechanism (self-attention) becomes computationally too expensive.

The idea of Longformer is to use sparse attention instead of dense attention. Instead of computing attention between all pairs of tokens (which has quadratic complexity), Longformer restricts attention to local windows and some global tokens.

Main features:

• Local attention: each token interacts only with a limited number of neighboring tokens (e.g., within a fixed-size window), significantly reducing computational load.
• Global attention: some special tokens (e.g., CLS token) can access the entire sequence, allowing the model to consider global context.

Thus, Longformer maintains the ability to consider context but with linear complexity relative to sequence length, enabling efficient processing of very long texts.

This is especially useful in NLP tasks where analyzing large documents is necessary, such as classification, information extraction, or question-answering.