Paper · arXiv 2402.14547
OmniPred: Language Models as Universal Regressors
Xingyou Song, Oscar Li, Chansoo Lee, Bangding, Yang, Daiyi Peng, Sagi Perel, Yutian Chen
OmniPred, a framework training language models as universal end-to-end regressors, outperforms traditional regression models using textual representations of parameters and values from diverse experimental data.
Abstract
Over the broad landscape of experimental design, regression has been a powerful tool to accurately predict the outcome metrics of a system or model given a set of parameters, but has been traditionally restricted to methods which are only applicable to a specific task. In this paper, we propose OmniPred, a framework for training language models as universal end-to-end regressors over (x,y) evaluation data from diverse real world experiments. Using data sourced from Google Vizier, one of the largest blackbox optimization databases in the world, our extensive experiments demonstrate that through only textual representations of mathematical parameters and values, language models are capable of very precise numerical regression, and if given the opportunity to train over multiple tasks, can significantly outperform traditional regression models.