@inproceedings{zhang-etal-2026-self,
    title = "Self-Reinforcing Controllable Synthesis of Rare Relational Data via {B}ayesian Calibration",
    author = "Zhang, Chongsheng  and
      Wang, Hao  and
      Yu, Zelong  and
      Arias, Esteban Garces  and
      Rodemann, Julian  and
      Zhang, Zhanshuo  and
      Li, Qilong  and
      Fan, Gaojuan  and
      Muandet, Krikamol  and
      Heumann, Christian",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.423/",
    pages = "8690--8711",
    ISBN = "979-8-89176-395-1",
    abstract = "Imbalanced data are commonly present in real-world applications. While data synthesis can effectively mitigate data scarcity for rare classes, and LLMs have revolutionized text generation, the application of LLMs to the synthesis of relational/structured tabular data remains underexplored. Moreover, existing approaches lack an effective feedback mechanism to guide LLMs in continuously optimizing the quality of the generated data throughout the synthesis process. In this work, we propose RDDG, \textbf{R}elational \textbf{D}ata generator with \textbf{D}ynamic \textbf{G}uidance, which is a unified in-context learning framework that employs progressive chain-of-thought (CoT) steps to generate tabular data for enhancing downstream imbalanced classification performance. RDDG first uses core set selection to identify representative samples from the original data, then utilizes in-context learning to discover the inherent patterns and correlations among attributes within the core set, and subsequently generates tabular data while preserving the aforementioned constraints. More importantly, it incorporates a self-reinforcing feedback mechanism that provides automatic assessments of the quality of the generated data, enabling continuous quality optimization throughout the generation process. Experimental results on multiple real and synthetic datasets demonstrate that RDDG outperforms existing approaches in both data fidelity and downstream imbalanced classification performance."
}