用智能路由让多个大模型协同工作，提升任务准确率。
ORI: O Routing Intelligence
- 根据任务特点动态选择最合适的模型处理请求
- 在MMLU上比最强单模型高2.7分，MuSR高1.8分
- 适合需要多模型协作的高性能推理系统
单一大型语言模型在应对日益增长的任务范围时往往表现不足，单模型方案已显局限。为此，我们提出ORI（O Routing Intelligence）——一种动态框架，利用一组大语言模型协同工作。通过智能路由将输入查询分配至最合适模型，ORI不仅提升了任务特定准确性，还保持了高效性。在多个基准上的全面评估表明，其在控制计算开销的同时实现了持续的准确率提升：在MMLU上相比最强单模型最高提升2.7分，在MuSR上提升1.8分，于ARC和BBH上达到顶尖水平。这些结果凸显了多模型策略的优势，证明了ORI自适应架构在处理多样化任务时的高效性，为多大模型系统提供了一种可扩展、高性能的解决方案。
原文摘要 · Abstract (English)
Single large language models (LLMs) often fall short when faced with the ever-growing range of tasks, making a single-model approach insufficient. We address this challenge by proposing ORI (O Routing Intelligence), a dynamic framework that leverages a set of LLMs. By intelligently routing incoming queries to the most suitable model, ORI not only improves task-specific accuracy, but also maintains efficiency. Comprehensive evaluations across diverse benchmarks demonstrate consistent accuracy gains while controlling computational overhead. By intelligently routing queries, ORI outperforms the strongest individual models by up to 2.7 points on MMLU and 1.8 points on MuSR, ties the top performance on ARC, and on BBH. These results underscore the benefits of a multi-model strategy and demonstrate how ORI's adaptive architecture can more effectively handle diverse tasks, offering a scalable, high-performance solution for a system of multiple large language models.
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