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

• URL: http://arxiv.org/abs/2505.23841v1
• Date: Wed, 28 May 2025 14:45:56 GMT
• Title: SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context
• Authors: Hairu Wang, Yuan Feng, Yukun Cao, Xike Xie, S Kevin Zhou,
• Abstract summary: Large language models excel at many tasks but often incur high inference costs during deployment.<n>A promising solution to balance performance and cost is LLM routing, which directs simple queries to smaller LLMs and complex ones to larger LLMs.<n>We propose a novel, training-free routing framework, the first tailored to KG-RAG that effectively balances performance and cost in a plug-and-play manner.
• Score: 19.447729423696096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models excel at many tasks but often incur high inference costs during deployment. To mitigate hallucination, many systems use a knowledge graph to enhance retrieval-augmented generation (KG-RAG). However, the large amount of retrieved knowledge contexts increase these inference costs further. A promising solution to balance performance and cost is LLM routing, which directs simple queries to smaller LLMs and complex ones to larger LLMs. However, no dedicated routing methods currently exist for RAG, and existing training-based routers face challenges scaling to this domain due to the need for extensive training data. We observe that the score distributions produced by the retrieval scorer strongly correlate with query difficulty. Based on this, we propose a novel, training-free routing framework, the first tailored to KG-RAG that effectively balances performance and cost in a plug-and-play manner. Experiments show our method reduces calls to larger LLMs by up to 50% without sacrificing response quality, demonstrating its potential for efficient and scalable LLM deployment.

Related papers

• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
  Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
  arXiv  Detail & Related papers  (2025-05-26T15:27:55Z)
• Smart Routing: Cost-Effective Multi-LLM Serving for Multi-Core AIOS [31.60019342381251]
  Existing scheduling frameworks mainly target at latency optimization.<n>This paper proposes an efficient capability-cost coordinated scheduling framework, ECCOS, for multi-LLM serving.
  arXiv  Detail & Related papers  (2025-02-27T22:35:31Z)
• Universal Model Routing for Efficient LLM Inference [72.65083061619752]
  We consider the problem of dynamic routing, where new, previously unobserved LLMs are available at test time.<n>We propose a new approach to this problem that relies on representing each LLM as a feature vector, derived based on predictions on a set of representative prompts.<n>We prove that these strategies are estimates of a theoretically optimal routing rule, and provide an excess risk bound to quantify their errors.
  arXiv  Detail & Related papers  (2025-02-12T20:30:28Z)
• MixLLM: Dynamic Routing in Mixed Large Language Models [57.309520357563215]
  Large Language Models (LLMs) exhibit potential artificial generic intelligence recently, however, their usage is costly with high response latency.<n>We develop MixLLM, a dynamic contextual-bandit-based routing system for query-LLM assignment.
  arXiv  Detail & Related papers  (2025-02-09T02:26:15Z)
• Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization [61.02719787737867]
  Large language models (LLMs) are increasingly deployed and democratized on edge devices.<n>One promising solution is uncertainty-based SLM routing, offloading high-stakes queries to stronger LLMs when resulting in low-confidence responses on SLM.<n>We conduct a comprehensive investigation into benchmarking and generalization of uncertainty-driven routing strategies from SLMs to LLMs over 1500+ settings.
  arXiv  Detail & Related papers  (2025-02-06T18:59:11Z)
• CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing [56.98081258047281]
  Collaborative Inference with Token-lEvel Routing (CITER) is a framework that enables efficient collaboration between small and large language models.<n>We formulate router training as a policy optimization, where the router receives rewards based on both the quality of predictions and the inference costs of generation.<n>Our experiments show that CITER reduces the inference costs while preserving high-quality generation, offering a promising solution for real-time and resource-constrained applications.
  arXiv  Detail & Related papers  (2025-02-04T03:36:44Z)
• Doing More with Less: A Survey on Routing Strategies for Resource Optimisation in Large Language Model-Based Systems [1.430963201405577]
  Large Language Model (LLM)-based systems are usually designed with a single, general-purpose LLM to handle all user queries.<n>These systems may be inefficient as different queries may require different levels of reasoning, domain knowledge or pre-processing.<n>A routing mechanism can therefore be employed to route queries to more appropriate components, such as smaller or specialised models.
  arXiv  Detail & Related papers  (2025-02-01T12:08:38Z)
• PickLLM: Context-Aware RL-Assisted Large Language Model Routing [0.5325390073522079]
  PickLLM is a lightweight framework that relies on Reinforcement Learning (RL) to route on-the-fly queries to available models.<n>We demonstrate the speed of convergence for different learning rates and improvement in hard metrics such as cost per querying session and overall response latency.
  arXiv  Detail & Related papers  (2024-12-12T06:27:12Z)
• Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation [43.630437906898635]
  We propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information.
  arXiv  Detail & Related papers  (2024-11-11T14:25:37Z)
• Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG [36.754491649652664]
  Retrieval-augmented generation (RAG) empowers large language models (LLMs) to utilize external knowledge sources. This paper investigates the detrimental impact of retrieved "hard negatives" as a key contributor. To mitigate this and enhance the robustness of long-context LLM-based RAG, we propose both training-free and training-based approaches.
  arXiv  Detail & Related papers  (2024-10-08T12:30:07Z)
• Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration [70.09561665520043]
  We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans. We provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems. Experiments on Over-AI and a difficult variant of RoCoBench show that ReAd surpasses baselines in success rate, and also significantly decreases the interaction steps of agents.
  arXiv  Detail & Related papers  (2024-05-23T08:33:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.