Generating realistic synthetic tabular data presents a critical challenge in
machine learning. This study introduces a simple yet effective method employing
Large Language Models (LLMs) tailored to generate synthetic data, specifically
addressing data imbalance problems. We propose a novel group-wise prompting
method in CSV-style formatting that leverages the in-context learning
capabilities of LLMs to produce data that closely adheres to the specified
requirements and characteristics of the target dataset. Moreover, our proposed
random word replacement strategy significantly improves the handling of
monotonous categorical values, enhancing the accuracy and representativeness of
the synthetic data. The effectiveness of our method is extensively validated
across eight real-world public datasets, achieving state-of-the-art performance
in downstream classification and regression tasks while maintaining
inter-feature correlations and improving token efficiency over existing
approaches. This advancement significantly contributes to addressing the key
challenges of machine learning applications, particularly in the context of
tabular data generation and handling class imbalance. The source code for our
work is available at: this https URL

通过使用大型语言模型（Large Language Models，LLMs）生成合成数据解决分类不平衡问题，在 CSV 格式中采用新颖的分组提示方法，利用 LLMs 的上下文学习能力生成满足目标数据集要求和特征的数据，并通过随机词替换策略提高处理单调分类值的准确性和代表性。在八个真实公共数据集上广泛验证我们的方法，取得了优于现有方法的下游分类和回归任务性能，同时保持特征间的相关性并提高标识的效率。该研究对于解决机器学习应用中关于表格数据生成和处理类别不平衡的关键挑战具有重要意义。