Large Language Models (LLMs) are increasingly used for synthetic tabular data generation through in-context learning (ICL), offering a practical solution for data augmentation in data scarce scenarios. While prior work has shown the potential of LLMs to improve downstream task performance through augmenting underrepresented groups, these benefits often assume access to a subset of unbiased in-context examples, representative of the real dataset. In real-world settings, however, data is frequently noisy and demographically skewed. In this paper, we systematically study how statistical biases within in-context examples propagate to the distribution of synthetic tabular data, showing that even mild in-context biases lead to global statistical distortions. We further introduce an adversarial scenario where a malicious contributor can inject bias into the synthetic dataset via a subset of in-context examples, ultimately compromising the fairness of downstream classifiers for a targeted and protected subgroup. Finally, we evaluate mitigation strategies based on preprocessing in-context examples, demonstrating that while such interventions can attenuate disparity, the inherent sensitivity of LLMs to adversarial prompts remains a persistent challenge. Our findings highlight a critical new vulnerability in LLM-based data generation pipelines within sensitive domains.

翻译：大型语言模型（LLMs）正日益通过上下文学习（ICL）被用于生成合成表格数据，为数据稀缺场景下的数据增强提供了实用解决方案。尽管先前的研究已展示LLMs通过增强代表性不足的群体来提升下游任务性能的潜力，但这些益处通常假设能够获取一部分无偏的上下文示例，且这些示例能代表真实数据集。然而在现实场景中，数据往往存在噪声且存在人口统计学上的偏斜。本文系统研究了上下文示例中的统计偏见如何传播至合成表格数据的分布，结果表明即使轻微的上下文偏见也会导致全局统计失真。我们进一步引入一种对抗性场景，其中恶意贡献者可通过一部分上下文示例将偏见注入合成数据集，最终损害下游分类器针对特定受保护子群体的公平性。最后，我们评估了基于上下文示例预处理的缓解策略，证明虽然此类干预措施能够减轻差异，但LLMs对对抗性提示的内在敏感性仍然是一个持续存在的挑战。我们的研究结果揭示了敏感领域中基于LLM的数据生成流程存在一个关键的新脆弱性。