RouterBench: A Benchmark for Multi-LLM Routing System
(2403.12031)Abstract
As the range of applications for LLMs continues to grow, the demand for effective serving solutions becomes increasingly critical. Despite the versatility of LLMs, no single model can optimally address all tasks and applications, particularly when balancing performance with cost. This limitation has led to the development of LLM routing systems, which combine the strengths of various models to overcome the constraints of individual LLMs. Yet, the absence of a standardized benchmark for evaluating the performance of LLM routers hinders progress in this area. To bridge this gap, we present RouterBench, a novel evaluation framework designed to systematically assess the efficacy of LLM routing systems, along with a comprehensive dataset comprising over 405k inference outcomes from representative LLMs to support the development of routing strategies. We further propose a theoretical framework for LLM routing, and deliver a comparative analysis of various routing approaches through RouterBench, highlighting their potentials and limitations within our evaluation framework. This work not only formalizes and advances the development of LLM routing systems but also sets a standard for their assessment, paving the way for more accessible and economically viable LLM deployments. The code and data are available at https://github.com/withmartian/routerbench.
Overview
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Introduces RouterBench, a benchmark for evaluating Large Language Model (LLM) routing systems, offering a theoretical framework and extensive dataset for development and evaluation.
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Presents a mathematical framework for LLM router evaluation focusing on efficiency maximization and cost minimization, introducing concepts like linear interpolation and non-decreasing convex hull.
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RouterBench dataset includes over 405k inference results across a range of tasks, like commonsense reasoning and knowledge-based understanding, to train and test model routers effectively.
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Empirical analysis shows strategic routing can significantly enhance performance and reduce costs across multiple tasks, highlighting the potential of router systems and suggesting areas for future research.
RouterBench: Evaluating Multi-LLM Routing Systems with a Novel Benchmark
Introduction
The evolution of LLMs has been swift, presenting a diverse array of models optimized for an extensive range of tasks. This wealth of options, though beneficial, introduces the challenge of selecting the most appropriate model for a given application, a problem magnified by the varying costs associated with different models. To tackle this, the concept of LLM routing, which dynamically selects the optimal LLM for each task, has gained prominence. However, the absence of a standardized benchmark for evaluating these routing systems has limited progress in this domain. Addressing this gap, RouterBench is introduced as a specialized benchmark for assessing LLM routing systems, contributing a theoretical framework and an extensive dataset to advance the development and evaluation of routing strategies.
Theoretical Framework for LLM Routing Evaluation
To formalize the evaluation of LLM routers, a mathematical framework is established, focusing on the two primary objectives of efficiency maximization and cost minimization. This framework introduces operations such as linear interpolation, allowing for the weighted averaging between two router configurations, and extrapolation, which extends router analysis to a broader cost domain. Furthermore, it elaborates on the construction of a non-decreasing convex hull for router evaluation, setting a baseline performance metric (AIQ) for comparative analysis of different routers in a cost-quality plane. This structured approach enables a detailed assessment of router strategies, comparing them against an optimal Zero router benchmark.
Benchmark Construction and Datasets
The RouterBench dataset covers a wide range of tasks representative of current LLM applications, including commonsense reasoning, knowledge-based language understanding, and more. It comprises over 405k inference results from various LLMs, designed to facilitate the efficient training and testing of model routers. A noteworthy inclusion is the RAG dataset, aimed at evaluating routers in complex retrieval-augmented tasks, reflecting the challenges of deploying routers in "compound system" settings. This diversity and comprehensiveness ensure that RouterBench can effectively benchmark routers across a spectrum of scenarios.
Empirical Results and Insights
The analysis reveals significant findings about the current state of LLM routing. Notably, the benchmark demonstrates that while no single model consistently outperforms others across all tasks, strategic routing can significantly enhance performance and reduce costs. This underscores the potential of router systems to leverage the diversity of available LLMs efficiently. Additionally, the study identifies promising areas for future research, particularly in refining router designs to improve decision-making between models with differing capabilities and costs.
Future Directions and Conclusion
While RouterBench marks a significant step towards standardized router evaluation, it also highlights areas for further exploration. Future work will focus on expanding the benchmark to incorporate additional metrics, tasks, and models, enhancing our understanding of router systems' potential. Moreover, exploring advanced router designs and optimizing router strategies for specific applications are identified as key directions for research. In summary, RouterBench establishes a foundational framework for evaluating LLM routers, paving the way for advancements in the efficient and cost-effective deployment of language models.
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