AI RESEARCH
Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration
arXiv CS.LG
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ArXi:2604.16817v1 Announce Type: new Imbalanced data is commonly present in real-world applications. While data synthesis can effectively mitigate the data scarcity problem of rare-classes, and LLMs have revolutionized text generation, the application of LLMs to relational/structured tabular data synthesis remains underexplored. Moreover, existing approaches lack an effective feedback mechanism that can guide LLMs towards continuously optimizing the quality of the generated data throughout the synthesis process.