TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks

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 costefficiency.Our findings provides the LLM community with an efficient and scalable solution for model ensembling, offering users an evolvable "super model."with an application to corpus exploration.