Large Tabular Models: An Overlooked Opportunity for Advancing Machine Learning and Scientific Discovery

Core Concepts

Tabular data is ubiquitous in the real world, yet it has been significantly overlooked in the machine learning research community. Developing large, foundational tabular models (LTMs) could revolutionize how science and machine learning utilize tabular data, enabling few-shot learning, automated data science, synthetic data generation, and empowering multidisciplinary discovery.

Abstract

The author argues that tabular data, which is the dominant modality in many fields, has been given hardly any research attention and significantly lags behind in terms of scale and power compared to text and image foundation models. They propose the concept of a Large Tabular Model (LTM) - a tabular foundation model that can be trained on large, diverse datasets and adapted to a wide range of downstream tasks. The key points made in the paper are: Tabular data is ubiquitous in the real world, yet it is heavily underrepresented in current foundation model research. The author hypothesizes this is due to a lack of large tabular datasets, the inherent difficulty of tabular ML, and human perception biases towards text and vision. Developing LTMs could have a far-reaching impact, from few-shot tabular models to automating data science, generating synthetic data, and empowering multidisciplinary scientific discovery. LTMs could also aid in responsible AI by improving inclusiveness, representation, privacy, and robustness. The author outlines four key desiderata for LTMs: handling mixed-type columns, cross-dataset modeling, leveraging textual context, and being invariant/equivariant to column order. The current state of LTM research is limited, with most work focusing on representation learning or supervised learning. Generative LTMs are particularly lacking. Adapting large language models for tabular generation faces challenges around modeling continuous variables, poor calibration, and inefficiency. The author discusses the unique challenges in building and evaluating LTMs, including data diversity and quality, reliable intrinsic and extrinsic evaluation, and mitigating biases. The author compares the potential impact of LTMs and LLMs, arguing that while LLMs may have broader public and commercial use, LTMs could have a transformative impact on scientific research and data-driven industries.

Stats

"Tabular data is the dominant modality in many fields, yet it is given hardly any research attention and significantly lags behind in terms of scale and power." "Recent benchmarking papers find that XGBoost, a tree-based model, is still among the top performers for supervised learning on tabular data." "The largest datasets used in tabular benchmarks are only 50,000 points, while for LLMs and vision models, the training data is in the billions."

Quotes

"Tabular data is ubiquitous in the real world—from electronic healthcare records to census data, from cybersecurity to credit scoring, and from finance to natural sciences." "Developing SOTA LTMs is still within computational reach of many ML researchers, cf. the economically-exclusionary cost of training modern LLMs." "Humans are incredibly good at understanding text and image, and foundation models in the text and vision space have aimed to match this: to encapsulate fundamental understanding of abstract concepts, resembling human-like skills. In the tabular domain the power of FMs may be just as large—foundation models being able to reason about real-world distributions over different variables, and generalize to new relationships."

Key Insights Distilled From

Why Tabular Foundation Models Should Be a Research Priority

by Boris van Br... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01147.pdf

Deeper Inquiries

How can we ensure the diversity and quality of tabular datasets used to train LTMs, given the inherent heterogeneity and privacy concerns in many tabular domains?

To ensure the diversity and quality of tabular datasets used to train Large Tabular Models (LTMs), several strategies can be implemented: Curated Datasets: Curate diverse datasets from various sources to ensure representation across different domains and data types. This can help in capturing a wide range of features and contexts present in tabular data. Data Augmentation: Use data augmentation techniques to increase the diversity of the training data. This can involve techniques like adding noise, perturbing data points, or generating synthetic data to introduce variability. Privacy-Preserving Techniques: Implement privacy-preserving techniques to address concerns related to sensitive data. This can include anonymization, differential privacy, or federated learning approaches to protect individual privacy while still allowing for model training on diverse datasets. Bias Detection and Mitigation: Employ techniques to detect and mitigate bias in the training data. This can involve bias audits, fairness metrics, and bias correction methods to ensure that the model does not perpetuate or amplify existing biases present in the data. Data Validation and Verification: Implement rigorous data validation and verification processes to ensure the quality and reliability of the training data. This can involve data cleaning, outlier detection, and verification of data sources to maintain data integrity. By incorporating these strategies, researchers can ensure that the tabular datasets used to train LTMs are diverse, high-quality, and representative of the real-world scenarios they aim to model.

How can we ensure the diversity and quality of tabular datasets used to train LTMs, given the inherent heterogeneity and privacy concerns in many tabular domains?

To effectively model the complex, discontinuous, and high-dimensional nature of tabular data in Large Tabular Models (LTMs), novel architectural designs and training techniques may be required: Hybrid Architectures: Explore hybrid architectures that combine attention mechanisms with specialized modules for handling different types of tabular data (numerical, categorical, datetime). This can help in capturing the unique characteristics of tabular data and improving model performance. Sparse Attention Mechanisms: Develop sparse attention mechanisms that can efficiently handle high-dimensional and sparse tabular data. This can help in reducing computational complexity and improving the scalability of LTMs to large datasets. Graph Neural Networks: Consider leveraging Graph Neural Networks (GNNs) to model the relational structure present in tabular data. GNNs can capture dependencies between different features and entities in the data, enhancing the model's ability to reason over complex relationships. Adaptive Learning Rates: Implement adaptive learning rate schedules that can dynamically adjust the learning rate based on the characteristics of the tabular data. This can help in optimizing model training and improving convergence on discontinuous data distributions. Regularization Techniques: Explore novel regularization techniques tailored for tabular data, such as feature dropout, label smoothing, or domain-specific regularization terms. These techniques can help prevent overfitting and improve the generalization of LTMs to unseen data. By incorporating these architectural designs and training techniques, researchers can enhance the capability of LTMs to effectively model the intricate nature of tabular data and achieve superior performance on diverse and complex datasets.

How can LTMs be leveraged to enable new forms of multidisciplinary scientific collaboration and discovery by seamlessly bridging and reasoning over diverse tabular datasets from different fields?

Large Tabular Models (LTMs) can facilitate new forms of multidisciplinary scientific collaboration and discovery by bridging and reasoning over diverse tabular datasets from different fields in the following ways: Cross-Domain Knowledge Integration: LTMs can integrate knowledge from diverse tabular datasets across different fields, enabling researchers to uncover hidden relationships and patterns that may not be apparent within individual datasets. This can lead to new insights and discoveries that span multiple disciplines. Automated Data Harmonization: LTMs can automate the process of harmonizing and integrating tabular data from various sources, making it easier for researchers to combine and analyze datasets from different fields. This can streamline the data integration process and facilitate collaboration between researchers with diverse data backgrounds. Enhanced Data Exploration and Visualization: LTMs can assist researchers in exploring and visualizing complex tabular datasets, providing insights into the relationships between variables and enabling interactive data exploration. This can help researchers from different fields gain a deeper understanding of the data and collaborate more effectively on interdisciplinary projects. Facilitated Meta-Analyses: LTMs can support meta-analyses by aggregating and analyzing data from multiple tabular datasets, allowing researchers to conduct comprehensive analyses that span different fields and studies. This can lead to more robust and generalizable findings that contribute to the advancement of multidisciplinary scientific research. Knowledge Transfer and Transfer Learning: LTMs can facilitate knowledge transfer between different fields by transferring learnings and insights from one dataset to another. This can accelerate research progress, promote interdisciplinary collaboration, and foster innovation by leveraging the collective knowledge embedded in diverse tabular datasets. By leveraging LTMs to bridge and reason over diverse tabular datasets from different fields, researchers can unlock new opportunities for multidisciplinary scientific collaboration, discovery, and innovation, leading to impactful advancements in various domains.