When training and using traditional AI models, like ChatGPT, the trade-off has always been between size, resources and time. This means larger and newer models that deliver better performance come at a high cost and are time exhaustive to build and maintain.

Now, what if you can pre-train a model faster than the traditional ones that cost less and can deliver the same performance? That’s Mixture of Experts or MoE for you!

In this article, we’ll discover together what Mixture of Experts (MoE) is, how it works, its challenges, and the possible solutions and applications to this unique approach to scaling models.

Mixture of Experts (MoE) Explained

Mixture of Experts or MoE allows the pretraining of models at a less compute and resources. This means you can drastically scale up the model or dataset size using the same computational power and budget as a traditional model. A fully capable MoE model could achieve the same quality as its traditional counterpart much faster during pre-training, but it’s not guaranteed that it will surpass traditional models as it is dependent on size—e.g. an 8 billion parameter will not outperform a 1.2 trillion parameter.

The MoE is in the context of transformer models, consisting of two primary elements: Sparse MoE layers and a gate network (or router).

• Sparse MoE Layers: Sparse MoE Layers are primarily used instead of the typical dense feed-forward network (FFN). Each Sparse MoE Layers houses several “experts” (e.g. 8), with each expert being a neural network. These experts may vary in complexity and, interestingly, can include MoEs themselves, resulting in hierarchical MoE systems.
• Gate Network or Router: It is critical in defining how tokens are sent to appropriate experts. This routing is not only crucial to the MoE’s operation, but also introduces the complexity of decision-making concerning token routing, where the router is a learning entity that evolves throughout network pretraining process.

In MoEs, each FFN layer of the transformer model is replaced with an MoE layer—that’s made up of a gate network or router and a set of experts.

How Does MoE Work?

MoE’s unique approach works by partitioning the entire input into tokens, with each one handled by specific experts. Each of these experts are trained to their respective tokens with a router or gating network used to determine which expert it goes to, then consolidate the results to give you an output. This allows the model to leverage the strengths of each expert, leading to improved overall performance.

MoE models can be particularly effective when the input is large and complex, as they can capture a wide range of patterns and relationships.

The Challenges of Using MoEs

• Training Challenges: One big challenge of MoEs is that it struggles to generalize during the fine-tuning process. While it may deliver an impressive and efficient pretraining, it has shown instances of overfitting.
• Inference Challenges: Although large in parameter size, only a fraction of these parameters are active during inference, resulting in faster processing times. This, however, requires a significant amount of computer memory because all parameters must be stored into the RAM regardless of their status during inference.

Practical Applications MoE

MoEs have found applications in various fields, notably in regression and classification. It has also been effective in complex tasks such as image recognition and natural language processing.

• MoE can be used for regression tasks, with the goal is to predict a continuous output variable.
• MoE can be used for classification tasks, with the goal is to predict a categorical output variable.
• MoE can be used for image recognition tasks, where the goal is to identify objects or features in images.
• MoE can be used for natural language processing tasks, where the goal is to understand and generate human language.

Takeaways

Mixture of Experts has seen significant impact in the AI space, allowing the development of new, robust and bigger models while minimizing additional computational requirements. With the combined outputs of multiple models, MoE often achieves better performance than its traditional counterparts. However, as with any emerging technologies and techniques, it is important to use MoE responsibly to minimize bias and transparency difficulties.

MoE is still an emerging technique and faces challenges, especially relating to training and memory demands. Ongoing research is focused on advancing MoE methods to overcome these limitations. With further development, MoE has the potential to accelerate progress in core AI capabilities.

Mixture of Experts also promises to expand access to state-of-the-art AI.This could possibly democratize leading-edge AI capabilities. If its full potential is realized, MoE could profoundly expand the horizons of what is possible with AI.

References:

• https://huggingface.co/blog/moe
• https://blog.gopenai.com/mixture-of-experts-moe-in-ai-models-explained-2163335eaf85
• https://medium.com/@juanc.olamendy/unraveling-the-complexity-of-mixture-of-experts-moe-in-machine-learning-7db682d76b00
• https://deepgram.com/learn/mixture-of-experts-ml-model-guide#mixtureofexperts-the-classic-approach
• https://klu.ai/glossary/mixture-of-experts
• https://machinelearningmastery.com/mixture-of-experts/