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Summary of Eagle: Efficient Training-free Router For Multi-llm Inference, by Zesen Zhao et al.


Eagle: Efficient Training-Free Router for Multi-LLM Inference

by Zesen Zhao, Shuowei Jin, Z. Morley Mao

First submitted to arxiv on: 23 Sep 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: None

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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 introduces a novel approach to Large Language Model (LLM) routing called Eagle. The goal is to efficiently select the most suitable LLM for a given query based on task requirements and budget constraints. Existing routers face scalability and real-time adaptation challenges, particularly in high-volume online environments. Eagle combines global and local ELO ranking modules to overcome these limitations. It evaluates both general and specialized LLM abilities, providing a scalable, training-free solution that enhances model selection quality while reducing computational overhead. Experimental results across multiple datasets show Eagle outperforms baseline methods by up to 23.52 percent in Area Under Curve (AUC) scores, with remarkable efficiency requiring only 1/20 of baseline methods’ time for initialization and 100 to 200 times faster incremental updates.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper is about finding the best Large Language Model (LLM) for a job. LLMS are like super smart computers that can understand and generate human-like text. Right now, there are many different LLMS with different abilities and costs. This makes it hard to choose the right one for a task. The authors of this paper created a new way called Eagle to help solve this problem. Eagle is fast, efficient, and works well even in situations where there are many requests at once. It’s like having a personal assistant that helps you find the best LLM for your needs.

Keywords

» Artificial intelligence  » Auc  » Large language model