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SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context

A training-free routing framework for knowledge graph retrieval-augmented generation.

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SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context

By Wang, Hairu, Yuan Feng, Yukun Cao, Xie, Xike, S Kevin ZhouarXiv (Cornell University)
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The authors propose a simple and effective routing framework, SkewRoute, which balances performance and cost in knowledge graph retrieval-augmented generation. The framework is designed to direct queries to the most suitable language models based on score skewness of retrieved contexts.

It achieves over 3x higher routing effectiveness while reducing runtime compared to existing methods.

Abstract

The authors propose a simple and effective routing framework, SkewRoute, which balances performance and cost in knowledge graph retrieval-augmented generation. The framework is designed to direct queries to the most suitable language models based on score skewness of retrieved contexts. It achieves over 3x higher routing effectiveness while reducing runtime compared to existing methods.

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skewroutellm routingknowledge graph retrieval-augmented generationtraining-free frameworkperformance and cost balanceKnowledge GraphsRetrieval & RAGLarge Language ModelsContent Engineering
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