Chain-of-experts (CoE): A lower-cost LLM framework that increases efficiency and accuracy

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Enterprises increasingly rely on large language models (LLMs) to deliver advanced services, but struggle to handle the computational costs of running models. A new framework, chain-of-experts (CoE), aims to make LLMs more resource-efficient while increasing their accuracy on reasoning tasks. The CoE framework addresses the limitations of earlier approaches by activating “experts” — separated elements of a model, each specializing in certain tasks — sequentially instead of in parallel. This structure allows experts to communicate intermediate results and gradually build on each others’ work. Architectures such as CoE can become very useful in inference-intensive applications, where gains in efficiency can result in huge cost savings and better user experience. Dense LLMs and mixture-of-experts Classic LLMs, sometimes referred to as dense models, activate ev ... Read full article.