Summary of Omnipred: Language Models As Universal Regressors, by Xingyou Song et al.
OmniPred: Language Models as Universal Regressors
by Xingyou Song, Oscar Li, Chansoo Lee, Bangding Yang, Daiyi Peng, Sagi Perel, Yutian Chen
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB)
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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 proposes OmniPred, a framework for training language models as universal end-to-end regressors. The authors use data from Google Vizier, one of the largest proprietary blackbox optimization databases, to demonstrate that language models can perform very precise numerical regression using textual representations of mathematical parameters and values. They also show that if trained at scale over multiple tasks, language models can outperform traditional regression models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use language models for predicting numbers based on text descriptions. The authors tested this idea with data from Google Vizier and found that it works really well. In the past, people had to use special tools for each specific task they wanted to predict, but OmniPred makes it possible to use one tool for many tasks. This could make it easier to get accurate predictions in the future. |
Keywords
* Artificial intelligence * Optimization * Regression
