Fusing Models with Complementary Expertise
Hongyi Wang Felipe Maia Polo Yuekai Sun Souvik Kundu Eric Xing Mikhail Yurochkin
Training AI models that generalize across tasks and domains has long been
among the open problems driving AI research. The emergence of Foundation Models
made it easier to obtain expert models for a given task, but the heterogeneity
of data that may be encountered at test time often means that any single expert
is insufficient. We consider the Fusion of Experts (FoE) problem of fusing
outputs of expert models with complementary knowledge of the data distribution
and formulate it as an instance of supervised learning. Our method is
applicable to both discriminative and generative tasks and leads to significant
performance improvements in image and text classification, text summarization,
multiple-choice QA, and automatic evaluation of generated text. We also extend
our method to the "frugal" setting where it is desired to reduce the number of
expert model evaluations at test time.