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Mixture-of-experts models explained: What you need to know

By combining specialized models to handle complex tasks, mixture-of-experts architectures can improve efficiency and accuracy for large language models and other AI systems.

Popular chatbots such as ChatGPT, Claude and Gemini are tasked with responding to a wide range of user queries on practically any topic imaginable. But achieving both broad and deep expertise on so many subjects is challenging for even the largest machine learning models.

Mixture-of-experts models are designed to tackle this challenge. MoE architectures combine the capabilities of multiple specialized models, known as experts, within a single overarching system. The idea behind the MoE architecture is to break up complex tasks into smaller, simpler pieces, which are then completed by the expert best suited to each subtask.

The MoE approach differs from a monolithic machine learning architecture, where the same model completes all tasks. Monolithic models sometimes struggle with diverse inputs that require different types of expertise -- a common scenario for many consumer-facing generative AI tools. By combining the abilities of several smaller experts, rather than relying on one enormous model to complete all tasks, MoE models can offer better overall accuracy and efficiency.

It's similar to the concept of microservices vs. monolithic architecture in software development. Dividing a large system into smaller, more flexible components designed to serve specific purposes can improve performance and scalability. For a less technical example, think of an MoE model as akin to a panel of human experts convened to review a draft policy. Each expert provides input on their area of focus: A physician weighs in on medical matters, an attorney handles questions of law and so on.

How do mixture-of-experts models work?

MoE is a form of ensemble learning, a machine learning technique that combines predictions from multiple models to improve overall accuracy. An MoE system has two main components:

  • Experts. These smaller models are trained to perform well in a certain domain or on a specific type of problem. They can have virtually any underlying algorithm, from a complex neural network to a simple decision tree, depending on their intended purpose. The number of experts in an MoE model can vary widely based on the complexity of the overall system and the available data and compute.
  • Gating mechanisms. The gating mechanism in an MoE model -- sometimes referred to as the gating network -- functions similarly to a router, deciding which experts to activate in response to a given input and combining their outputs to generate the final result. After evaluating the input, the gating mechanism calculates a probability distribution that indicates each expert's suitability for the task. The system then selects the most appropriate experts, assigns weights to their contributions and integrates their outputs into a final response.

When the MoE model receives an input, the gating mechanism assesses that input to determine which experts should handle the task, then routes the input to the selected experts. Next, the experts analyze the input and generate their respective outputs, which are combined using a weighted sum to form the ultimate decision.

By dynamically assigning tasks to different experts, the MoE architecture can take advantage of the strengths of each expert, improving the system's overall adaptability and performance. Notably, the MoE system can engage multiple experts to varying extents for the same task. The gating mechanism manages this process by directing queries to the right experts and deciding how much importance to assign each expert's contribution in the final output.

Training an MoE model involves optimizing both the expert models and the gating mechanism. Each expert is trained on a different subset of the overall training data, enabling these models to develop specialized knowledge bases and problem-solving capabilities. Meanwhile, the gating mechanism is taught how to effectively assess inputs so that it can assign tasks to the most appropriate experts.

Examples of mixture-of-experts model applications

MoE models have a wide range of use cases:

  • Natural language processing. The ability to assign tasks such as translation, sentiment analysis and question answering to specialized experts makes MoE models useful for language-related problems. For example, reports suggest that OpenAI's GPT-4 large language model uses an MoE architecture comprising 16 experts, though OpenAI has not officially confirmed details of the model's design.
  • Computer vision. MoE models can assist in image processing and machine vision by assigning subtasks to different image experts -- for example, to handle specific object categories, types of visual features or image regions.
  • Recommender systems. Recommendation engines powered by MoE models are able to adapt to user interests and preferences. For example, an MoE-powered recommender could assign different experts to respond to various customer segments, handle product categories and account for contextual factors.
  • Anomaly detection. Because experts in an MoE system are trained on narrower data subsets, they can learn to specialize in detecting specific types of anomalies. This improves overall sensitivity and enables the anomaly detection model to handle more types of data inputs.

Pros and cons of mixture-of-experts models

Compared with monolithic models, MoE models have several advantages:

  • Performance. The ability to call on specialized experts is key to MoE models' effectiveness and efficiency. Because only relevant experts are activated for a given task, not every component of the model is typically running at once. This leads to more efficient computational processing and memory use.
  • Adaptability. Experts' extensive capabilities make MoE models highly flexible. By calling on experts with specialized capabilities, the MoE model can succeed on a wider range of tasks.
  • Modularity and fault tolerance. As discussed above, microservices architectures can improve flexibility and availability in software, and an MoE structure can play a similar role in machine learning contexts. If one expert fails, the system can still potentially return useful responses by combining other experts' outputs. Likewise, model developers can add, remove or update experts as needed in response to changing data and evolving user needs.
  • Scalability. Decomposing complex problems into smaller, more manageable tasks helps MoE models handle increasingly difficult or complicated inputs. And thanks to their modularity, MoE models can also be expanded to handle additional types of problems by adding new experts or retraining existing ones.

However, despite these advantages, MoE models also have certain challenges and limitations:

  • Complexity. MoE models require a great deal of infrastructure resources, both for training and at inference time, because managing multiple experts as well as the gating mechanism is computationally expensive. MoE models' complexity also makes them more challenging to train and maintain, as developers must integrate and update multiple smaller models and ensure that they work well together within a cohesive whole.
  • Overfitting. While the specialized nature of the experts is key to MoE systems' usefulness, too much specialization can be damaging. If the training data set isn't sufficiently diverse or if the expert is trained on too narrow a subset of the overall data, the expert could overfit to its specific domain, reducing its accuracy on previously unseen data and downgrading the system's overall performance.
  • Interpretability. Opacity is already a notable problem in AI, including for leading LLMs. An MoE architecture can worsen this problem because it adds complexity; rather than following only one monolithic model's decision-making process, those attempting to understand an MoE model's decision must also unpack the complex interactions among the various experts and gating mechanism.
  • Data requirements. To train the experts and optimize the gating mechanism, MoE models require extensive, diverse, well-structured training data. Acquiring, storing and preparing that data can be challenging, especially for entities with fewer resources, such as smaller organizations and academic researchers.

Future directions in mixture-of-experts research

In the coming years, MoE research is likely to focus on improving efficiency and interpretability, optimizing how experts collaborate with one another, and developing better methods for task allocation.

With regard to MoE models' complexity and resource needs, developers are exploring techniques for improving hardware and algorithmic efficiency. For example, distributed computing architectures spread the MoE system's computational load across multiple machines, and model compression can reduce the size of expert models without significantly impairing their performance. At inference time, developers can also reduce computational demands by incorporating techniques such as sparsity, which activates only a small subset of experts in response to each input.

In terms of interpretability, research in explainable AI -- a field focused on making models' decision-making processes clearer -- could potentially be applied to MoE models. Insights into the decision-making of both experts and gating mechanisms would offer greater clarity regarding how MoE systems arrive at their ultimate output. This could mean, for instance, developing gating mechanisms that show how particular experts were chosen or constructing experts that can offer explanations for their decisions.

Lev Craig covers AI and machine learning as the site editor for TechTarget Enterprise AI. Craig graduated from Harvard University with a bachelor's degree in English and has previously written about enterprise IT, software development and cybersecurity.

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