@inproceedings{chen-etal-2025-tagrouter,
    title = "{T}ag{R}outer: Learning Route to {LLM}s through Tags for Open-Domain Text Generation Tasks",
    author = "Chen, Zhou  and
      Wei, Zhiqiang  and
      Bai, Yuqi  and
      Xiong, Xue  and
      Wu, Jianmin",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.findings-acl.1110/",
    doi = "10.18653/v1/2025.findings-acl.1110",
    pages = "21539--21564",
    ISBN = "979-8-89176-256-5",
    abstract = "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.''"
}