OmniPred：作为通用回归器的语言模型
OmniPred: Language Models as Universal Regressors
AI总结：
本文提出OmniPred框架，将语言模型训练为处理任意格式$(x,y)$数据的通用端到端回归器，基于Google Vizier数据库的实验表明，其在大规模多任务训练下能显著优于传统回归模型。
摘要：
回归是一种强大的工具，能够给定一组参数准确预测系统的结果指标，但传统上仅限于适用于特定任务的方法。在本文中，我们提出OmniPred，一个将语言模型训练为通用端到端回归器的框架，可处理任意格式的$(x,y)$数据。使用来自Google Vizier（世界上最大的专有黑盒优化数据库之一）的数据，我们的大量实验表明，语言模型仅使用数学参数和值的文本表示就能进行非常精确的数值回归，并且如果有机会在多任务上进行大规模训练，可以显著优于传统回归模型。
英文摘要：
Regression is a powerful tool to accurately predict the outcome metric of a system given a set of parameters, but has traditionally been 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 $(x,y)$ data from arbitrary formats. Using data sourced from Google Vizier, one of the largest proprietary blackbox optimization databases in the world, our extensive experiments demonstrate that language models are capable of very precise numerical regression using only textual representations of mathematical parameters and values, and if given the opportunity to train at scale over multiple tasks, can significantly outperform traditional regression models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
