LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation

2026-08-03

AI Summary

This work addresses few-shot table generation in cross-domain settings where target-domain samples are scarce but source-domain data are abundant—a scenario in which existing methods often suffer from overfitting or erroneous transfer of irrelevant dependencies. To tackle this challenge, the authors propose LAB-Tab, a novel framework that introduces large language models (LLMs) into Bayesian network structure learning for the first time. LAB-Tab first constructs an initial network from the source domain and then leverages an LLM, guided by semantic cues and weak statistical evidence, to generate candidate edge structures for the target domain. Fine-grained, edge-level editing is performed via proximal policy optimization (PPO) to adaptively refine cross-domain dependencies. Evaluated across six cross-domain transfer tasks, LAB-Tab achieves state-of-the-art performance using only 10% of the target data, outperforming the strongest baseline by a significant margin—reducing the overall score by 33.8% and excelling on metrics including Jensen-Shannon Divergence (JSD), WAPE, and UtilityGap.

Abstract

Tabular data generation supports analysis and decision-making when target-domain data are scarce, yet collecting complete target samples is often costly. A practical but underexplored setting provides only a few target records together with richer source data from a related domain. Existing few-shot tabular generators often either fit sparse target statistics directly, which can overfit incidental patterns, or reuse source-domain generators, which may preserve dependencies that no longer hold in the target domain. To address this problem, we propose LAB-Tab, an LLM-augmented Bayesian network (BN) adaptation framework for source-aware few-shot tabular generation. LAB-Tab first fits a BN from source data and then uses an LLM to propose plausible target-domain BN edges that are absent from the source BN graph. This step converts semantic and weak statistical evidence into explicit structural hypotheses, thereby expanding the editable edge space beyond the source-fitted graph. Because the proposed edges may be noisy and interact with existing dependencies, a PPO policy calibrates edges in the augmented BN through edge-level actions, including keep, weaken, strengthen, flip, and deactivate. The PPO policy is trained with a reward that combines distributional alignment, downstream utility, and preservation of target-relevant dependencies. The adapted BN is then sampled to synthesize target-domain tables. Across six source--target distribution-shift scenarios built from three US Census (ACS) prediction tasks, LAB-Tab achieves the best performance at the 10% target-data budget, leads four of the six individual scenarios, and reduces the macro Overall score by 33.8% relative to the strongest baseline. It also obtains the best macro JSD, WAPE, and UtilityGap while maintaining competitive feature--label preservation.

Problem

• few-shot tabular generation
• distribution shift
• Bayesian network adaptation
• source-target domain
• tabular data synthesis

Innovation

• LLM-augmented Bayesian network
• few-shot tabular generation
• distribution shift adaptation
• PPO-based structure calibration
• cross-domain tabular synthesis