Table Foundation Models: on knowledge pre-training for tabular learning

• URL: http://arxiv.org/abs/2505.14415v2
• Date: Mon, 30 Jun 2025 15:48:16 GMT
• Title: Table Foundation Models: on knowledge pre-training for tabular learning
• Authors: Myung Jun Kim, Félix Lefebvre, Gaëtan Brison, Alexandre Perez-Lebel, Gaël Varoquaux,
• Abstract summary: TARTE is a foundation model that transforms tables to knowledge-enhanced vector representations using the string.<n>Pre-trained on large relational data, TARTE yields representations that facilitate subsequent learning with little additional cost.
• Score: 47.485516405457595
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Table foundation models bring high hopes to data science: pre-trained on tabular data to embark knowledge or priors, they should facilitate downstream tasks on tables. One specific challenge is that of data semantics: numerical entries take their meaning from context, e.g., column name. Pre-trained neural networks that jointly model column names and table entries have recently boosted prediction accuracy. While these models outline the promises of world knowledge to interpret table values, they lack the convenience of popular foundation models in text or vision. Indeed, they must be fine-tuned to bring benefits, come with sizeable computation costs, and cannot easily be reused or combined with other architectures. Here we introduce TARTE, a foundation model that transforms tables to knowledge-enhanced vector representations using the string to capture semantics. Pre-trained on large relational data, TARTE yields representations that facilitate subsequent learning with little additional cost. These representations can be fine-tuned or combined with other learners, giving models that push the state-of-the-art prediction performance and improve the prediction/computation performance trade-off. Specialized to a task or a domain, TARTE gives domain-specific representations that facilitate further learning. Our study demonstrates an effective approach to knowledge pre-training for tabular learning.

Related papers

• Representation Learning for Tabular Data: A Comprehensive Survey [23.606506938919605]
  Tabular data, structured as rows and columns, is among the most prevalent data types in machine learning classification and regression applications.<n>Deep Neural Networks (DNNs) have recently demonstrated promising results through their capability of representation learning.<n>We organize existing methods into three main categories according to their generalization capabilities.
  arXiv  Detail & Related papers  (2025-04-17T17:58:23Z)
• TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes [25.169832192255956]
  We present TabFM, a neural tabular model for data discovery over data lakes.<n>We finetune the pretrained model for identifying unionable, joinable, and subset table pairs.<n>Our results demonstrate significant improvements in F1 scores for search compared to state-of-the-art techniques.
  arXiv  Detail & Related papers  (2024-06-28T17:28:53Z)
• Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning [63.850451635362425]
  Continual learning requires a model to adapt to ongoing changes in the data distribution. We show that the combination of a large language model and an image generation model can similarly provide useful premonitions. We find that the backbone of our pre-trained networks can learn representations useful for the downstream continual learning problem.
  arXiv  Detail & Related papers  (2024-03-12T06:29:54Z)
• Making Pre-trained Language Models Great on Tabular Prediction [50.70574370855663]
  The transferability of deep neural networks (DNNs) has made significant progress in image and language processing. We present TP-BERTa, a specifically pre-trained LM for tabular data prediction. A novel relative magnitude tokenization converts scalar numerical feature values to finely discrete, high-dimensional tokens, and an intra-feature attention approach integrates feature values with the corresponding feature names.
  arXiv  Detail & Related papers  (2024-03-04T08:38:56Z)
• Pre-training and Diagnosing Knowledge Base Completion Models [58.07183284468881]
  We introduce and analyze an approach to knowledge transfer from one collection of facts to another without the need for entity or relation matching. The main contribution is a method that can make use of large-scale pre-training on facts, which were collected from unstructured text. To understand the obtained pre-trained models better, we then introduce a novel dataset for the analysis of pre-trained models for Open Knowledge Base Completion.
  arXiv  Detail & Related papers  (2024-01-27T15:20:43Z)
• MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement [44.693325083735424]
  Tabular data prediction has been employed in medical applications such as patient health risk prediction. Previous predictors are often trained on manually curated small datasets.
  arXiv  Detail & Related papers  (2023-05-20T03:37:09Z)
• PTab: Using the Pre-trained Language Model for Modeling Tabular Data [5.791972449406902]
  Recent studies show that neural-based models are effective in learning contextual representation for Tabular data. We propose a novel framework PTab, using the Pre-trained language model to model Tabular data. Our method has achieved a better average AUC score in supervised settings compared to the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-09-15T08:58:42Z)
• Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models [67.48894919842576]
  State-of-the-art pretrained language models tend to perform below their capabilities when applied out-of-the-box on tasks that require numeracy. We propose a new extended pretraining approach called Arithmetic-Based Pretraining that jointly addresses both in one extended pretraining step. Our experiments show the effectiveness of Arithmetic-Based Pretraining in three different tasks that require improved numeracy.
  arXiv  Detail & Related papers  (2022-05-13T16:10:13Z)
• BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines [49.75878234192369]
  We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model. We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
  arXiv  Detail & Related papers  (2022-02-21T10:34:41Z)
• TABBIE: Pretrained Representations of Tabular Data [22.444607481407633]
  We devise a simple pretraining objective that learns exclusively from tabular data. Unlike competing approaches, our model (TABBIE) provides embeddings of all table substructures. A qualitative analysis of our model's learned cell, column, and row representations shows that it understands complex table semantics and numerical trends.
  arXiv  Detail & Related papers  (2021-05-06T11:15:16Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)
• TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data [113.29476656550342]
  We present TaBERT, a pretrained LM that jointly learns representations for NL sentences and tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. Implementation of the model will be available at http://fburl.com/TaBERT.
  arXiv  Detail & Related papers  (2020-05-17T17:26:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.