用径向结构提升大模型路由效率与鲁棒性
RadialRouter: Structured Representation for Efficient and Robust Large Language Models Routing
- 设计径向结构Transformer，建模查询与大模型间的关联关系
- 在RouterBench上比现有方法分别提升9.2%和5.8%的性能
- 支持动态模型池与多目标权衡，适合实际部署场景
大语言模型（LLMs）的快速发展催生了路由技术，旨在从多个候选模型中高效选择最适合特定任务的模型，以优化性能并降低成本。现有路由方法因未能充分挖掘用户查询与模型特性之间的内在联系，效果受限。为此，本文提出RadialRouter框架，采用轻量级Transformer骨干网络RadialFormer，通过径向结构显式表达查询与模型间的关联。最优模型的选择基于RadialFormer的最终状态。通过结合KL散度与查询-查询对比损失的目标函数，进一步提升了模型的鲁棒性。在RouterBench上的实验结果表明，RadialRouter在平衡（Balance）和成本优先（Cost First）场景下，分别比现有方法显著提升9.2%和5.8%。此外，其对不同性能-成本权衡及动态模型池的适应能力，展现了良好的实际应用潜力。
原文摘要 · Abstract (English)
The rapid advancements in large language models (LLMs) have led to the emergence of routing techniques, which aim to efficiently select the optimal LLM from diverse candidates to tackle specific tasks, optimizing performance while reducing costs. Current LLM routing methods are limited in effectiveness due to insufficient exploration of the intrinsic connection between user queries and the characteristics of LLMs. To address this issue, in this paper, we present RadialRouter, a novel framework for LLM routing which employs a lightweight Transformer-based backbone with a radial structure named RadialFormer to articulate the query-LLMs relationship. The optimal LLM selection is performed based on the final states of RadialFormer. The pipeline is further refined by an objective function that combines Kullback-Leibler divergence with the query-query contrastive loss to enhance robustness. Experimental results on RouterBench show that RadialRouter significantly outperforms existing routing methods by 9.2\% and 5.8\% in the Balance and Cost First scenarios, respectively. Additionally, its adaptability toward different performance-cost trade-offs and the dynamic LLM pool demonstrates practical application potential.
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