Which model type is known for having a higher parameter count compared to similar capacity dense models?

Mixture-of-Expert Models (MoE) are designed to improve the efficiency and capability of neural networks by incorporating a large number of parameters while only activating a small subset of them at any given time. This architecture enables MoE models to exhibit a higher parameter count compared to other dense models that have the same apparent capacity.

In a typical dense model, all parameters are engaged during the forward pass, which can make them less efficient when scaling up. In contrast, MoE models strategically select which "experts" (sub-networks) to activate based on the input data, allowing them to leverage a large number of parameters without the computational burden that would typically accompany that many active parameters.

This selective activation means that even though MoE models have a substantially higher parameter count, they can operate with efficiency by focusing computational resources only on relevant parts of the model during inference. This balanced trade-off allows them to harness the advantages of a larger parameter space while maintaining operational efficiency. Other model types, such as convolutional neural networks or recurrent neural networks, do not share this same architecture and operational principle, leading to a lower parameter count relative to their capacity.