Sparsity techniques improve LLMs by reducing the number of active parameters or operations in the model, which decreases computational and memory costs while maintaining performance. Sparse models activate only a subset of their parameters during computation, rather than processing all parameters for every input. This makes them more efficient for both training and inference.
Techniques like sparse attention focus computational effort on the most relevant parts of the input sequence, skipping less critical areas. For example, in a long document, sparse attention mechanisms prioritize relationships between key tokens while ignoring irrelevant ones. MoE (Mixture of Experts) models take this further by routing input to a small subset of "expert" layers, significantly reducing the computation needed for each input.
Sparsity enables scaling larger models without proportionally increasing resource demands. It is particularly beneficial for deploying LLMs in latency-sensitive environments or on devices with limited resources. These techniques ensure that LLMs remain efficient while handling large-scale tasks.