@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
Garces Arias, Esteban 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://aclanthology.org/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."
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%0 Conference Proceedings
%T Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration
%A Zhang, Chongsheng
%A Wang, Hao
%A Yu, Zelong
%A Garces Arias, Esteban
%A Rodemann, Julian
%A Zhang, Zhanshuo
%A Li, Qilong
%A Fan, Gaojuan
%A Muandet, Krikamol
%A Heumann, Christian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-self
%X 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, Relational Data generator with Dynamic Guidance, 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.
%U https://aclanthology.org/2026.findings-acl.423/
%P 8690-8711
Markdown (Informal)
[Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration](https://aclanthology.org/2026.findings-acl.423/) (Zhang et al., Findings 2026)
ACL
- Chongsheng Zhang, Hao Wang, Zelong Yu, Esteban Garces Arias, Julian Rodemann, Zhanshuo Zhang, Qilong Li, Gaojuan Fan, Krikamol Muandet, and Christian Heumann. 2026. Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration. In Findings of the Association for Computational Linguistics: ACL 2026, pages 8690–8711, San Diego, California, United States. Association for Computational Linguistics.