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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
classification cs.CL
keywords routingfeasibleapproachescapabilitieschallengingdevelopmentdirectefficiently
verification ladder T0 review T1 audit T2 compute T3 formal
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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 4 Pith papers

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

  1. Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process

    cs.CL 2025-12 unverdicted novelty 5.0 of 10

    LLM-PeerReview ensembles LLMs by scoring responses with LLM-as-Judge and selecting the best via averaging or truth inference, beating Smoothie-Global by 6.9-7.3 points on four datasets.

  2. Balancing Information Accuracy and Response Timeliness in Networked LLMs

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    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. A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement

    cs.CL 2025-07 unverdicted novelty 5.0 of 10

    SMCS coordinates 15 open-source LLMs via retrieval-based prior selection and exploration-exploitation posterior enhancement, outperforming GPT-4.1 by 5.36% and GPT-o3-mini by 5.28% on eight benchmarks.

  4. Harnessing Multiple Large Language Models: A Survey on LLM Ensemble

    cs.CL 2025-02 unverdicted novelty 2.0 of 10

    A systematic survey of LLM ensemble methods organized into a taxonomy of ensemble-before-inference, ensemble-during-inference, and ensemble-after-inference stages, with review of benchmarks, applications, and future d...

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