OmniPred: Language Models as Universal Regressors

Abstract: 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 LLMs 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 LLMs 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.

• The paper introduces a novel approach that converts regression tasks into a text processing problem using large language models.
• The methodology employs a key-value text format and temperature decoding, eliminating the need for extensive feature engineering.
• Experimental results on datasets like Google Vizier demonstrate OmniPred's effective transfer learning and multi-task performance over traditional models.

LLMs as Capable Predictors in Universal Regression Tasks

Introduction

Regression tasks have been integral to numerous scientific and industrial applications, aiming to predict continuous outcomes based on a set of input variables. Traditional regression models, while powerful in their specific domains, often require substantial customization and feature engineering to adapt to new tasks. OmniPred introduces a novel approach to regression, harnessing the flexibility and scalability of LLMs to serve as universal end-to-end regressors. By leveraging textual representations of experimental parameters and outcomes, OmniPred showcases the potential for LLMs to perform accurate metric predictions across a diverse array of real-world datasets, notably outperforming traditional models in many instances.

Technical Approach

The methodology detailed in OmniPred focuses on transforming regression into a text processing task. Given the varied nature of data in experimental settings, the paper crafts a specialized representation of both input parameters and target metrics in textual format. This transformative step allows leveraging LLMs—in this case, a T5 model with 200 million parameters—for regression tasks without the need for explicit feature engineering or normalization typically seen in traditional models.

Key aspects of the methodology include:

• Task Representation: Utilizing a key-value text format for input parameters and a custom tokenization for numerical outcomes.
• Model Training: Adopting a standard cross-entropy loss function, similar to conventional LLM training but specified towards numerical regression.
• Sampling and Decoding: Implementing temperature decoding to generate outcome distributions, showcasing the model's utility in approximating the underlying distribution of results.

Experimental Insights

OmniPred was rigorously evaluated against both synthetic and real-world datasets, demonstrating its capability to learn and predict across tasks with varying input spaces and objective scales. One notable experiment included data from Google Vizier, a comprehensive source for blackbox optimization tasks, illustrating OmniPred's superior performance compared to traditional regression models on unseen tasks, further emphasizing the model’s adaptability and potential for generalization.

Implications and Future Directions

The paper’s findings suggest significant implications for the field of experimental design and beyond:

• Transfer Learning: OmniPred's ability to leverage textual representations allows it to benefit from transfer learning, significantly improving performance on tasks with little to immediate prior data.
• Multi-Task Learning: Demonstrating superior performance in multi-task settings over single-task models, OmniPred paves the way for more efficient and scalable modeling approaches in data-rich environments.
• Practical Applications: From hyperparameter tuning to complex system predictions, OmniPred’s framework suggests a shift towards more flexible and adaptive regression models, potentially reducing the reliance on domain-specific knowledge for feature engineering.

While OmniPred sets an exciting precedent, it also opens avenues for future research. Improvements could include exploring diverse input space representations, further refining the textual representation of numerical values, and investigating the utility of pre-trained models on regression tasks. Moreover, considering the computational overhead of LLMs, optimizing model efficiency without compromising on prediction accuracy remains a critical challenge.

Concluding Remarks

OmniPred represents a pioneering step towards universal regression models using LLMs. By successfully applying LLMs to a wide range of regression tasks, this work introduces a new paradigm in predictive modeling, blending the fields of natural language processing and quantitative prediction. While challenges remain, OmniPred's framework offers a compelling vision for the future of experimental design and predictive analytics, underlining the untapped potential of LLMs in quantitative domains.