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In the context of Large Language Models (LLMs) that use a Mixture-of-Experts (MoE) architecture, the 'experts' are typically implemented as modular Feed-Forward Networks (FFNs). Each expert functions as a distinct part of the FFN component within the overall Transformer architecture.

Experts as Modular FFNs in LLM MoE Models

A common building block in many large language models consists of a multi-head attention mechanism followed by a single, dense position-wise feed-forward network (FFN). In a 'mixture-of-experts' (MoE) variant of this architecture, the single FFN is replaced by a collection of multiple 'expert' networks. Analyze the relationship between the single FFN in the standard architecture and the collection of expert networks in the MoE architecture. What specific component do the experts replace, and how does their collective function compare to that of the original component?

Analysis of Expert Networks in Language Model Architecture

A standard transformer-based language model layer consists of a self-attention mechanism followed by a feed-forward network (FFN). An alternative architecture aims for greater parameter capacity and computational efficiency by using a routing mechanism to selectively activate one of several specialized 'expert' sub-networks within each layer for a given input. Based on this design, which component of the standard transformer layer are these 'expert' sub-networks most directly implementing and parallelizing?

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