Mixture-of-Experts (MoE) has revolutionized the way large language models (LLMs) are scaled, offering a more efficient solution to handling computational costs. However, traditional MoE architectures have faced limitations when it comes to accommodating a large number of experts. This constraint has restricted the scalability of MoE models, hindering their ability to fully leverage the benefits of increased parameter count.
To address the challenges faced by current MoE techniques, Google DeepMind has introduced Parameter Efficient Expert Retrieval (PEER). This novel architecture is designed to scale MoE models to millions of experts, significantly improving the performance-compute tradeoff of large language models. By replacing the fixed router with a learned index, PEER efficiently routes input data to a vast pool of experts, enabling the MoE to handle a large number of experts without compromising speed.
In traditional transformer architectures used in LLMs, Feedforward (FFW) layers play a crucial role in storing the model’s knowledge. However, the sheer size of FFW layers poses a bottleneck when it comes to scaling transformers. The computational footprint of FFW layers is directly proportional to their size, making them inefficient for handling large language models.
Unlike previous MoE architectures, PEER utilizes tiny experts with a single neuron in the hidden layer. This design allows the model to share hidden neurons among experts, promoting knowledge transfer and parameter efficiency. Additionally, PEER employs a multi-head retrieval approach to select and activate top experts, further enhancing the performance of MoE models.
The performance of PEER models has been evaluated across different benchmarks, showcasing its superiority over transformer models with dense feedforward layers and other MoE architectures. Through experiments, researchers have demonstrated that PEER models achieve a better performance-compute tradeoff, leading to lower perplexity scores with the same computational budget. Increasing the number of experts in a PEER model has also shown to reduce perplexity further, highlighting the scalability and efficiency of the architecture.
The advent of PEER has challenged the conventional wisdom that MoE models are limited by the number of experts they can accommodate. By leveraging advanced retrieval and routing mechanisms, PEER has paved the way for scaling MoE to millions of experts, offering a cost-effective and streamlined solution for training and serving very large language models. With the potential to dynamically add new knowledge and features to LLMs, PEER represents a significant breakthrough in the evolution of Mixture-of-Experts architectures.
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