Large language models (LLMs) are now available from cloud API providers in various sizes and configurations. While this diversity offers a broad spectrum of choices, effectively leveraging the options to optimize computational cost and performance remains challenging. In this work, we present Automix, an approach that strategically routes queries to larger LMs, based on the approximate correctness of outputs from a smaller LM. Central to Automix are two key technical contributions. First, it has a few-shot self-verification mechanism, which estimates the reliability of its own outputs without requiring extensive training. Second, given that self-verification can be noisy, it employs a POMDP based router that can effectively select an appropriately sized model, based on answer confidence. Experiments across five language models and five challenging datasets show that Automix consistently surpasses strong baselines, reducing computational cost by over 50% for comparable performance.
@article{arxiv.2310.12963,
title = {AutoMix: Automatically Mixing Language Models},
author = {Pranjal Aggarwal and Aman Madaan and Ankit Anand and Srividya Pranavi Potharaju and Swaroop Mishra and Pei Zhou and Aditya Gupta and Dheeraj Rajagopal and Karthik Kappaganthu and Yiming Yang and Shyam Upadhyay and Manaal Faruqui and Mausam},
journal= {arXiv preprint arXiv:2310.12963},
year = {2025}
}
Comments
38th Conference on Neural Information Processing Systems (NeurIPS 2024). The first two authors contributed equally. Work started and partly done during Aman's internship at Google. This version adds results on additional models and datasets