TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks
Zhou Chen, Zhiqiang Wei, Yuqi Bai, Xue Xiong, Jianmin Wu
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.”
BibTeX
@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://aclanthology.org/2025.findings-acl.1110/",
doi = "10.18653/v1/2025.findings-acl.1110",
pages = "21539--21564",
ISBN = "979-8-89176-256-5"
}