用规则引导生成，解决小数据下表格合成的偏差与冗余问题。
Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
- 提取可解释模型的条件规则，嵌入提示词引导生成符合领域分布的数据。
- 双粒度过滤机制有效减少重复采样，保留稀有但关键样本。
- 在极低数据场景下平均性能提升7.48%，适合小样本领域应用。
合成表格数据在机器学习中日益重要，尤其在真实高质量数据不足时支持下游任务。现有方法如生成对抗网络（GANs）和微调的大语言模型（LLMs）通常需要充足参考数据，限制了其在记录稀少的特定领域数据集中的应用。尽管基于提示的LLMs无需参数调优，但常生成分布漂移且局部重复的数据，导致下游任务性能下降。为此，我们提出ReFine框架：(i) 从可解释模型中提取if-then规则，并将其嵌入提示词，显式引导生成过程贴近领域分布；(ii) 采用双粒度过滤机制，缓解过度采样模式，同时保留稀有但信息丰富的样本，降低局部冗余。在多种基准上的实验表明，ReFine具备稳健的下游效用，在不同数据集和数据环境下平均排名顶尖，极端低数据场景下平均相对提升达7.48%。
原文摘要 · Abstract (English)
Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient. Existing tabular generation approaches, such as generative adversarial networks (GANs) and fine-tuned Large Language Models (LLMs), typically require sufficient reference data, limiting their effectiveness in domain-specific datasets with scarce records. While prompt-based LLMs offer flexibility without parameter tuning, they often generate distributionally drifted data with localized redundancy, leading to degradation in downstream task performance. To overcome these issues, we propose ReFine, a framework that (i) extracts symbolic if-then rules from interpretable models and embeds them into prompts to explicitly guide the generation process toward the domain-specific distribution, and (ii) applies dual-granularity filtering that mitigates over-sampling patterns while preserving rare but informative samples to reduce localized redundancy. Extensive experiments on diverse benchmarks demonstrate that ReFine provides robust downstream utility, achieving a top-tier average rank across datasets and data regimes, with an average relative improvement of 7.48% in extreme low-data regimes.
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