标题： 提示设计是否会影响LLMs的数据填补质量？ 显示英文标题

标题： Does Prompt Design Impact Quality of Data Imputation by LLMs?

摘要： 生成逼真的合成表格数据在机器学习中提出了一个关键挑战。当这些数据包含类别不平衡问题时，这又增加了一层复杂性。 本文提出了一种新的基于标记感知的数据填补方法，该方法利用了大型语言模型的上下文学习能力。这是通过结合结构化的组别CSV风格提示技术和消除输入提示中的无关上下文信息来实现的。 我们使用两个类别不平衡的二元分类数据集测试了这种方法，并使用基于分类的评估指标评估了填补的有效性。 实验结果表明，与我们的基线提示相比，我们的方法在保持或提高填补质量的同时，显著减少了输入提示的大小，特别是在相对较小的数据集上尤其如此。 这项工作的贡献是双重的——1）它揭示了在利用LLMs进行合成数据生成时提示设计的重要性；2）它通过提供一种在计算约束内的实用解决方案，解决了LLM在处理类别不平衡且有缺失数据的数据集时数据填补的关键空白。 我们希望我们的工作能促进关于利用LLMs和提示工程技巧进行合成数据生成的进一步研究和讨论。 显示英文摘要

摘要： Generating realistic synthetic tabular data presents a critical challenge in machine learning. It adds another layer of complexity when this data contain class imbalance problems. This paper presents a novel token-aware data imputation method that leverages the in-context learning capabilities of large language models. This is achieved through the combination of a structured group-wise CSV-style prompting technique and the elimination of irrelevant contextual information in the input prompt. We test this approach with two class-imbalanced binary classification datasets and evaluate the effectiveness of imputation using classification-based evaluation metrics. The experimental results demonstrate that our approach significantly reduces the input prompt size while maintaining or improving imputation quality compared to our baseline prompt, especially for datasets that are of relatively smaller in size. The contributions of this presented work is two-fold -- 1) it sheds light on the importance of prompt design when leveraging LLMs for synthetic data generation and 2) it addresses a critical gap in LLM-based data imputation for class-imbalanced datasets with missing data by providing a practical solution within computational constraints. We hope that our work will foster further research and discussions about leveraging the incredible potential of LLMs and prompt engineering techniques for synthetic data generation.