用草图引导大模型生成公式，自动填补数据中的衍生缺失值
SketchFill: Sketch-Guided Code Generation for Imputing Derived Missing Values
- 通过草图框架引导大模型理解数据间数学关系
- 比CoT方法高56.2%准确率，比MetaGPT高78.8%准确率
- 适合需要精准补全数值数据的科研与工程场景
缺失值是数据科学中的关键问题，严重影响分析与预测的可靠性。缺失值填补（MVI）长期面临挑战，因其高度依赖领域知识。大语言模型（LLMs）在表格数据清洗中展现出潜力，具备内容理解与生成能力。然而，现有基于上下文学习和思维链（CoT）的方法在处理需数学公式与跨行列表达关系的衍生缺失值时表现不足。为此，我们提出SketchFill——一种基于草图的新方法，引导LLMs生成准确的填补公式。实验表明，SketchFill显著优于现有方法：相比CoT提升56.2%准确率，相比MetaGPT提升78.8%，为自动化数据清洗设定了新标准，推动数值型缺失值填补技术发展。
原文摘要 · Abstract (English)
Missing value is a critical issue in data science, significantly impacting the reliability of analyses and predictions. Missing value imputation (MVI) is a longstanding problem because it highly relies on domain knowledge. Large language models (LLMs) have emerged as a promising tool for data cleaning, including MVI for tabular data, offering advanced capabilities for understanding and generating content. However, despite their promise, existing LLM techniques such as in-context learning and Chain-of-Thought (CoT) often fall short in guiding LLMs to perform complex reasoning for MVI, particularly when imputing derived missing values, which require mathematical formulas and data relationships across rows and columns. This gap underscores the need for further advancements in LLM methodologies to enhance their reasoning capabilities for more reliable imputation outcomes. To fill this gap, we propose SketchFill, a novel sketch-based method to guide LLMs in generating accurate formulas to impute missing numerical values. Our experimental results demonstrate that SketchFill significantly outperforms state-of-the-art methods, achieving 56.2% higher accuracy than CoT-based methods and 78.8% higher accuracy than MetaGPT. This sets a new standard for automated data cleaning and advances the field of MVI for numerical values.
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