通过标签实现大模型路由，提升生成效率并降低成本
TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks
- 基于标签构建无需训练的路由机制，动态分配查询至合适模型
- 系统接受率提升6.15%，成本降低17.20%，达到最佳性价比
- 适合需要高效集成多模型的开放域文本生成场景
模型路由通过将查询分配给合适的模型来提升系统性能并降低计算成本。然而，现有路由方法在大规模应用中面临可扩展性瓶颈，难以跟上大语言模型（LLM）生态的快速演进。为此，我们提出TagRouter，一种无需训练的模型路由方法，旨在优化多个LLM在开放域文本生成任务中的协同效果。实验表明，TagRouter优于13种基线方法，使系统接受率提升6.15%，成本降低17.20%，实现最优成本效益。该研究为大模型集成提供了高效且可扩展的解决方案，助力用户构建可演进的‘超级模型’。
原文摘要 · Abstract (English)
Model routing allocates queries to the suitable model, improving system performance while reducing costs. However, existing routing methods face practical limitations that hinder scalability in large-scale applications and struggle to keep up with the rapid growth of the large language model (LLM) ecosystem. To tackle these challenges, we propose TagRouter, a training-free model routing method designed to optimize the synergy among multiple LLMs for open-domain text generation tasks. Experimental results demonstrate that TagRouter outperforms 13 baseline methods, increasing the accept rate of system by 6.15% and reducing costs by 17.20%, achieving optimal cost-efficiency. Our findings provides the LLM community with an efficient and scalable solution for model ensembling, offering users an evolvable "super model."
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