Summary of Routerbench: a Benchmark For Multi-llm Routing System, by Qitian Jason Hu et al.
RouterBench: A Benchmark for Multi-LLM Routing System
by Qitian Jason Hu, Jacob Bieker, Xiuyu Li, Nan Jiang, Benjamin Keigwin, Gaurav Ranganath, Kurt Keutzer, Shriyash Kaustubh Upadhyay
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitations of Large Language Models (LLMs) in addressing various tasks and applications while balancing performance with cost. To overcome these constraints, LLM routing systems combine strengths from multiple models. However, a standardized benchmark for evaluating LLM router performance is lacking, hindering progress. The authors present RouterBench, an evaluation framework designed to assess the efficacy of LLM routing systems, along with a comprehensive dataset comprising over 405k inference outcomes. A theoretical framework for LLM routing and comparative analysis of various routing approaches are also proposed, highlighting their potentials and limitations within the RouterBench framework. This work formalizes and advances LLM routing system development while setting a standard for assessment, enabling more accessible and economically viable LLM deployments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are super smart computer programs that can do lots of things like understand language and answer questions. But they’re not perfect and sometimes struggle to do certain tasks or jobs. To help solve this problem, researchers created special systems called LLM routing systems. These systems use multiple models to work together and make the LLMs better at doing different tasks. However, there’s no standard way to test how well these systems work. The authors of this paper want to fix that by creating a new tool called RouterBench. This tool will help scientists figure out which LLM routing system works best for a particular job. They also share a big dataset with lots of examples and propose ways to make the LLM routing systems better. |
Keywords
* Artificial intelligence * Inference
