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Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing

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arxiv 2405.00467 v1 pith:6BTVNXXV submitted 2024-05-01 cs.CL

KV Aditya Srivatsa , Kaushal Kumar Maurya , Ekaterina Kochmar This is my paper · ORCID
classification cs.CL
keywords routingfeasibleapproachescapabilitieschallengingdevelopmentdirectefficiently
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With the rapid development of LLMs, it is natural to ask how to harness their capabilities efficiently. In this paper, we explore whether it is feasible to direct each input query to a single most suitable LLM. To this end, we propose LLM routing for challenging reasoning tasks. Our extensive experiments suggest that such routing shows promise but is not feasible in all scenarios, so more robust approaches should be investigated to fill this gap.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A routing system that chooses both the model and the number of samples per query to meet a quality threshold, yielding up to 60% cost savings.

  2. Balancing Information Accuracy and Response Timeliness in Networked LLMs

    cs.LG 2025-08 conditional novelty 5.0 of 10

    For binary questions, combining m specialized LLMs with a Bayesian majority rule improves accuracy, and the paper derives the optimal m that trades accuracy against system delay.

  3. Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques

    cs.LG 2025-06 unverdicted novelty 4.0 of 10

    A survey of LLM routing and hierarchical inference techniques that proposes an unvalidated unified evaluation metric called the Inference Efficiency Score.

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