Unlocking AI Efficiency: The Chain-of-Experts Approach

Large language models (LLMs) are crucial for modern businesses. They power advanced services but come with high computational costs. This is where the chain-of-experts (CoE) framework steps in. It makes LLMs more efficient and accurate. The CoE framework tackles the limitations of earlier methods by activating "experts" sequentially. These experts are specialized parts of the model. They work together step-by-step, sharing intermediate results. This approach is different from traditional methods that activate all experts at once. CoE is particularly useful in applications that require a lot of processing power. These include tasks that need complex reasoning. By improving efficiency, CoE can lead to significant cost savings and a better user experience. Traditional LLMs, also known as dense models, activate all parameters at once during processing. This leads to high computational demands. Mixture-of-experts (MoE) models, like DeepSeek-V3 and GPT-4, try to solve this by splitting the model into experts. A router selects a subset of experts for each input, reducing computational overhead. However, MoE models have their own issues. Each expert works independently, which can hurt performance on tasks that need coordination. Additionally, MoE models have high memory requirements, even though only a small subset of experts is active at any given time. The CoE framework addresses these problems by activating experts one after the other. This allows experts to build on each other's work, providing context-aware inputs. This method enhances the model's ability to handle complex reasoning tasks and optimizes resource use by minimizing redundant computations. In mathematical reasoning or logical inference, CoE allows each expert to build on previous insights. This improves accuracy and task performance. CoE also optimizes resource use by minimizing redundant computations, addressing enterprise demands for cost-efficient and high-performing AI solutions. The CoE approach has several key benefits. It uses sequential activation and expert collaboration, which improves model performance while maintaining computational efficiency. This is especially true in complex scenarios, like mathematical tasks. Researchers found that CoE models outperform dense LLMs and MoEs with equal resources. For example, a CoE with 64 experts, four routed experts, and two inference iterations outperforms an MoE with 64 experts and eight routed experts in mathematical benchmarks. CoE also reduces memory requirements and allows for more efficient model architectures. CoE's lower operational costs and improved performance on complex tasks make advanced AI more accessible to enterprises. This helps them remain competitive without substantial infrastructure investments. This research opens new pathways for efficiently scaling language models, potentially making advanced artificial intelligence capabilities more accessible and sustainable.