ChatPaper.aiChatPaper

Tabby: Tabular Data Synthesis with Language Models

March 4, 2025
Authors: Sonia Cromp, Satya Sai Srinath Namburi GNVV, Mohammed Alkhudhayri, Catherine Cao, Samuel Guo, Nicholas Roberts, Frederic Sala
cs.AI

Abstract

While advances in large language models (LLMs) have greatly improved the quality of synthetic text data in recent years, synthesizing tabular data has received relatively less attention. We address this disparity with Tabby, a simple but powerful post-training modification to the standard Transformer language model architecture, enabling its use for tabular dataset synthesis. Tabby enables the representation of differences across columns using Gated Mixture-of-Experts, with column-specific sets of parameters. Empirically, Tabby results in data quality near or equal to that of real data. By pairing our novel LLM table training technique, Plain, with Tabby, we observe up to a 44% improvement in quality over previous methods. We also show that Tabby extends beyond tables to more general structured data, reaching parity with real data on a nested JSON dataset as well.

Summary

AI-Generated Summary

Paper Overview

Core Contribution

  • Introduces Tabby, a post-training modification to transformer-based LLMs for tabular data synthesis.
  • Uses Gated Mixture-of-Experts (MoE) layers to model column-specific parameters.
  • Achieves synthetic data quality near or equal to real data.
  • Extends beyond tables to general structured data (e.g., nested JSON).

Research Context

  • Tabular data synthesis has received less attention compared to text and image synthesis.
  • Challenges include complex column interdependencies, mixed modalities, and spurious correlations.
  • Prior approaches include GANs, LLMs, and diffusion models, but require significant preprocessing.

Keywords

  • Tabular data synthesis
  • Language models (LLMs)
  • Mixture-of-Experts (MoE)
  • Gated MoE
  • Structured data
  • Plain training technique

Background

Research Gap

  • Lack of specialized architectures for tabular data synthesis.
  • Limited focus on structured data modalities beyond tables.
  • Need for models that handle mixed data types and complex dependencies.

Technical Challenges

  • Modeling complex interdependencies between columns.
  • Handling mixed modalities (text, numerical, nested data).
  • Avoiding spurious correlations due to column order.

Prior Approaches

  • GANs (e.g., CTGAN, TVAE) struggle with mode collapse and complex distributions.
  • Diffusion models (e.g., Tab-DDPM) require strong assumptions and preprocessing.
  • LLMs (e.g., GReaT, TapTap, Tabula) focus on training techniques but lack architectural modifications.

Methodology

Technical Architecture

  • Tabby replaces select LLM blocks with MoE layers, allowing column-specific parameter sets.
  • MoE layers increase model expressivity for tabular data.
  • Plain training technique simplifies LLM fine-tuning for tabular data.

Implementation Details

  • Tabby modifies the language modeling head or transformer MLPs/attention blocks.
  • Plain training encodes tabular data as text with specialized tokens (<EOC>, <EOS>).
  • Training process calculates losses per column, enabling per-column performance tracking.

Innovation Points

  • First architecture modification to make LLMs better-suited for table generation.
  • Combines MoE layers with LLMs for higher-fidelity synthetic data.
  • Extends to nested JSON and other structured data modalities.

Results

Experimental Setup

  • Evaluated on six tabular datasets (Diabetes, Travel, Adult, Abalone, Rainfall, House) and one nested JSON dataset.
  • Metrics: Machine Learning Efficacy (MLE), Discrimination, Distance to Closest Record (DCR).
  • Compared with GANs, diffusion models, and prior LLM-based approaches.

Key Findings

  • Tabby achieves MLE parity with real data on 4/6 tabular datasets.
  • Plain-trained Tabby models outperform prior methods, including Tab-DDPM.
  • Tabby extends to nested JSON data, achieving parity with real data.
  • Smaller Tabby models outperform larger non-Tabby LLMs.

Limitations

  • Tabby’s parameter count scales with the number of columns, though parameter sharing can mitigate this.
  • Plain training, while effective, may not handle all dataset complexities.
  • Limited evaluation on extremely high-dimensional datasets.

Featured Papers

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Li Dong, Ruiping Wang, Jilong Xue, Furu WeiFeb 27, 2024610142

Qwen2.5 Technical Report

Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, Zihan QiuDec 19, 202435311

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, Jiawei Wang, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, J. L. Cai, Jiaqi Ni, Jian Liang, Jin Chen, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Liang Zhao, Litong Wang, Liyue Zhang, Lei Xu, Leyi Xia, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Meng Li, Miaojun Wang, Mingming Li, Ning Tian, Panpan Huang, Peng Zhang, Qiancheng Wang, Qinyu Chen, Qiushi Du, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, R. J. Chen, R. L. Jin, Ruyi Chen, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shengfeng Ye, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, S. S. Li, Shuang Zhou, Shaoqing Wu, Shengfeng Ye, Tao Yun, Tian Pei, Tianyu Sun, T. Wang, Wangding Zeng, Wanjia Zhao, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, W. L. Xiao, Wei An, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaotao Nie, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xuheng Lin, X. Q. Li, Xiangyue Jin, Xiaojin Shen, Xiaosha Chen, Xiaowen Sun, Xiaoxiang Wang, Xinnan Song, Xinyi Zhou, Xianzu Wang, Xinxia Shan, Y. K. Li, Y. Q. Wang, Y. X. Wei, Yang Zhang, Yanhong Xu, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Yu, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yuan Ou, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yunfan Xiong, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yaohui Li, Yi Zheng, Yuchen Zhu, Yunxian Ma, Ying Tang, Yukun Zha, Yuting Yan, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhicheng Ma, Zhigang Yan, Zhiyu Wu, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, Zhen ZhangJan 22, 20253485

PDF42March 5, 2025