2 papers
cs.LG2026
Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data
Zilong Zhao, Abdul Raheem, Jiayu Li +5
Synthetic data generation is dominated by the fit-then-sample paradigm: a generative model is trained on a private dataset and then sampled from. Despite its widespread adoption, t…
cs.CV2025
Instruction Tuning of Large Language Models for Tabular Data Generation-in One Day
Milad Abdollahzadeh, Abdul Raheem, Zilong Zhao +5
Tabular instruction tuning has emerged as a promising research direction for improving LLMs understanding of tabular data. However, the majority of existing works only consider que…