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

ACL ARR 2025 February Submission6726 Authors

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."

Paper Type: Long

Research Area: Efficient/Low-Resource Methods for NLP

Research Area Keywords: NLP in resource-constrained settings, data-efficient training

Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models

Languages Studied: Chinese, English

Submission Number: 6726