@inproceedings{wang-etal-2026-semirouter,
title = "{SEMIROUTER}: Sparse-Data Enhanced Routing for Adaptive Multi-{LLM} System",
author = "Wang, Zijie and
Yan, Xinyu and
Wang, Che and
Zihao, Zeng and
Xiao, Lei and
Lim, Wei Yang Bryan",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.228/",
pages = "4910--4921",
ISBN = "979-8-89176-380-7",
abstract = "Large Language Models (LLMs) exhibit remarkable capabilities, but no single model optimally balances serving quality and deployment cost across diverse tasks. Multi-LLM systems address this challenge through intelligent routing mechanisms that dynamically allocate queries to the most appropriate model. However, existing routing methods suffer from two fundamental limitations: (i) dependence on extensive full-response datasets for training, and (ii) poor scalability when incorporating new models, typically necessitating retraining from scratch. In this paper, we propose SemiRouter, a novel LLM routing framework designed for data-sparse and evolving model environments. Our approach combines a data-efficient training methodology with an adaptive architecture that enables seamless integration of new models under limited supervision. As an extension, we also consider energy footprint as a potential deployment cost in our routing decision. Empirical evaluations demonstrate that our method improves data efficiency, adaptability, and routing accuracy compared to existing approaches, providing a scalable solution for dynamic multi-LLM deployment."
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%0 Conference Proceedings
%T SEMIROUTER: Sparse-Data Enhanced Routing for Adaptive Multi-LLM System
%A Wang, Zijie
%A Yan, Xinyu
%A Wang, Che
%A Zihao, Zeng
%A Xiao, Lei
%A Lim, Wei Yang Bryan
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F wang-etal-2026-semirouter
%X Large Language Models (LLMs) exhibit remarkable capabilities, but no single model optimally balances serving quality and deployment cost across diverse tasks. Multi-LLM systems address this challenge through intelligent routing mechanisms that dynamically allocate queries to the most appropriate model. However, existing routing methods suffer from two fundamental limitations: (i) dependence on extensive full-response datasets for training, and (ii) poor scalability when incorporating new models, typically necessitating retraining from scratch. In this paper, we propose SemiRouter, a novel LLM routing framework designed for data-sparse and evolving model environments. Our approach combines a data-efficient training methodology with an adaptive architecture that enables seamless integration of new models under limited supervision. As an extension, we also consider energy footprint as a potential deployment cost in our routing decision. Empirical evaluations demonstrate that our method improves data efficiency, adaptability, and routing accuracy compared to existing approaches, providing a scalable solution for dynamic multi-LLM deployment.
%U https://aclanthology.org/2026.eacl-long.228/
%P 4910-4921
Markdown (Informal)
[SEMIROUTER: Sparse-Data Enhanced Routing for Adaptive Multi-LLM System](https://aclanthology.org/2026.eacl-long.228/) (Wang et al., EACL 2026)
ACL
- Zijie Wang, Xinyu Yan, Che Wang, Zeng Zihao, Lei Xiao, and Wei Yang Bryan Lim. 2026. SEMIROUTER: Sparse-Data Enhanced Routing for Adaptive Multi-LLM System. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4910–4921, Rabat, Morocco. Association for Computational Linguistics.