TabuLa: Harnessing Language Models for Tabular Data Synthesis

• URL: http://arxiv.org/abs/2310.12746v3
• Date: Mon, 17 Feb 2025 08:39:05 GMT
• Title: TabuLa: Harnessing Language Models for Tabular Data Synthesis
• Authors: Zilong Zhao, Robert Birke, Lydia Chen,
• Abstract summary: Tabula is a tabular data synthesizer that leverages the structure of large language models (LLMs) Unlike state-of-the-art (SOTA) LLMs, Tabula discards the pre-trained weights originally designed for natural language tasks. experiments show that Tabula achieves superior synthetic data utility compared to current SOTA methods.
• Score: 4.539846270369207
• License:
• Abstract: Tabular data synthesis is crucial for addressing privacy and security concerns in industries reliant on tabular data. While recent advancements adopt large language models (LLMs) for realistic tabular data generation, their long training times and limited reusability hinder practical applications. In this paper, we propose Tabula, a tabular data synthesizer that leverages the structure of LLM. Unlike state-of-the-art (SOTA) LLM-based tabular data synthesizers that rely on pre-trained LLMs, Tabula discards the pre-trained weights originally designed for natural language tasks, focusing instead on a tailored approach for tabular data. In addition, Tabula introduces a token sequence compression strategy that significantly reduces training time while maintaining data quality, alongside a novel token padding method that improves sequence alignment across training batches. Experiments on six datasets show that Tabula achieves superior synthetic data utility compared to current SOTA methods. Additionally, the results demonstrate that Tabula model trained on tabular datasets serves effectively as a foundational model for synthesizing new tabular datasets. Furthermore, the proposed padding method outperforms the conventional left and right padding strategies. Finally, the results highlight that Tabula averagely reduces training time per epoch by 46.2% compared to state-of-the-art LLM approaches while achieving higher data utility. Our code is available at https://github.com/zhao-zilong/Tabula

Related papers

• Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes [135.68092471784516]
  We propose a simple and lightweight approach for fusing large language models and gradient-boosted decision trees. We name our fusion methods LLM-Boost and PFN-Boost, respectively. We demonstrate state-of-the-art performance against numerous baselines and ensembling algorithms.
  arXiv  Detail & Related papers  (2025-02-04T19:30:41Z)
• Generating Realistic Tabular Data with Large Language Models [49.03536886067729]
  Large language models (LLM) have been used for diverse tasks, but do not capture the correct correlation between the features and the target variable. We propose a LLM-based method with three important improvements to correctly capture the ground-truth feature-class correlation in the real data. Our experiments show that our method significantly outperforms 10 SOTA baselines on 20 datasets in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-29T04:14:32Z)
• TabDPT: Scaling Tabular Foundation Models [20.00390825519329]
  We show how to harness the power of real data to improve performance and generalization. Our model achieves state-of-the-art performance on the CC18 (classification) and CTR23 (regression) benchmarks. TabDPT also demonstrates strong scaling as both model size and amount of available data increase.
  arXiv  Detail & Related papers  (2024-10-23T18:00:00Z)
• TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks [30.922069185335246]
  We find two common characteristics of tabular data in typical industrial applications that are underrepresented in the datasets usually used for evaluation in the literature. A considerable portion of datasets in production settings stem from extensive data acquisition and feature engineering pipelines. This can have an impact on the absolute and relative number of predictive, uninformative, and correlated features compared to academic datasets.
  arXiv  Detail & Related papers  (2024-06-27T17:55:31Z)
• LaTable: Towards Large Tabular Models [63.995130144110156]
  Tabular generative foundation models are hard to build due to the heterogeneous feature spaces of different datasets. LaTable is a novel diffusion model that addresses these challenges and can be trained across different datasets. We find that LaTable outperforms baselines on in-distribution generation, and that finetuning LaTable can generate out-of-distribution datasets better with fewer samples.
  arXiv  Detail & Related papers  (2024-06-25T16:03:50Z)
• Mixture of In-Context Prompters for Tabular PFNs [33.76194735049027]
  MIXTUREPFN is the Condorcet winner across 36 diverse datasets against 19 strong deep learning and tree-based baselines. It achieves the highest mean rank among Top-10 aforementioned algorithms with statistical significance.
  arXiv  Detail & Related papers  (2024-05-25T09:47:59Z)
• Rethinking Pre-Training in Tabular Data: A Neighborhood Embedding Perspective [71.45945607871715]
  We propose Tabular data Pre-Training via Meta-representation (TabPTM) The core idea is to embed data instances into a shared feature space, where each instance is represented by its distance to a fixed number of nearest neighbors and their labels. Extensive experiments on 101 datasets confirm TabPTM's effectiveness in both classification and regression tasks, with and without fine-tuning.
  arXiv  Detail & Related papers  (2023-10-31T18:03:54Z)
• Generative Table Pre-training Empowers Models for Tabular Prediction [71.76829961276032]
  We propose TapTap, the first attempt that leverages table pre-training to empower models for tabular prediction. TapTap can generate high-quality synthetic tables to support various applications, including privacy protection, low resource regime, missing value imputation, and imbalanced classification. It can be easily combined with various backbone models, including LightGBM, Multilayer Perceptron (MLP) and Transformer.
  arXiv  Detail & Related papers  (2023-05-16T06:37:38Z)
• 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)
• 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.