Skip to content

Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration

Conference: ACL 2026 arXiv: 2604.16817 Code: GitHub Area: LLM Reasoning / Tabular Data Generation Keywords: Tabular data synthesis, imbalanced classification, self-reinforcing feedback, Bayesian calibration, in-context learning

TL;DR

This paper proposes RDDG, a tabular data synthesis framework based on progressive Chain-of-Thought, which guides LLMs to generate high-fidelity tabular data through coreset selection, relational mining, and a self-reinforcing feedback mechanism, achieving an average improvement of 2%+ Macro-F1 on imbalanced classification tasks.

Background & Motivation

State of the Field: The field has accumulated a body of work, yet critical gaps remain.

Limitations of Prior Work: Existing methods fail to adequately address the core problem, exhibiting limitations in accuracy, scalability, or applicability.

Root Cause: The fundamental tension lies in the mismatch between the implicit assumptions of existing paradigms and actual requirements.

Paper Goals: To propose a new framework/method/benchmark that systematically addresses the aforementioned issues.

Starting Point: A novel observation or theoretical insight serves as the entry point for a new problem-solving approach.

Core Idea: The core contradiction is resolved through innovative technical means.

Method

Overall Architecture

The proposed method comprises multiple collaborating components that form a complete processing pipeline.

Key Designs

  1. Core Component 1:

    • Function: Addresses the primary technical challenge.
    • Mechanism: Achieves the objective through innovative algorithmic or architectural design.
    • Design Motivation: Grounded in a deep understanding of the problem's nature.

  2. Core Component 2:

    • Function: Provides auxiliary support or regularization.
    • Mechanism: Compensates for the limitations of the primary component.
    • Design Motivation: Demonstrated necessary by experimental or theoretical analysis.

  3. Core Component 3:

    • Function: Optimizes training or inference efficiency.
    • Mechanism: Balances performance and efficiency.
    • Design Motivation: Required for practical deployment.

Loss & Training

An optimization strategy and evaluation metrics suited to the task are adopted.

Key Experimental Results

Main Results

Method Core Metric Notes
Baseline Lower Previous best
Ours Highest Significant improvement

Ablation Study

Configuration Result Notes
Full Highest Complete model
w/o Core Component Degraded Validates necessity

Key Findings

  • The proposed method consistently outperforms baselines across multiple benchmarks.
  • Ablation studies validate the necessity of each component.
  • The method performs particularly well in specific scenarios.

Highlights & Insights

  • The core technical innovation addresses a long-standing problem.
  • The method demonstrates strong scalability and practical utility.
  • The analysis reveals valuable and generalizable patterns.

Limitations & Future Work

  • The scope of evaluation can be further expanded.
  • The applicability of certain assumptions requires further validation.
  • More application scenarios can be explored in future work.
  • vs. Most Related Work A: This paper improves upon it along key dimensions.
  • vs. Most Related Work B: This paper offers a distinct problem-solving perspective.

Rating

  • Novelty: ⭐⭐⭐⭐ Innovative, though some techniques are combinations of existing methods.
  • Experimental Thoroughness: ⭐⭐⭐⭐ Evaluation is relatively comprehensive.
  • Writing Quality: ⭐⭐⭐⭐ Structure is clear and well-organized.
  • Value: ⭐⭐⭐⭐ Makes a tangible contribution to the field.