用大模型自动生成数据清洗流程，提升效率与准确性
AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark
- 输入原始数据表和分析目标，自动输出OpenRefine操作序列
- 在142个任务中，Gemma 2-27B生成的表格质量最优，准确率高
- 适合数据科学家快速构建可复现的数据清洗工作流
数据清洗是耗时且易出错的手动过程，即使使用OpenRefine等现代工具也是如此。本文提出AutoDCWorkflow，一个基于大模型的数据清洗流程自动生成管道。该管道接收原始表格和数据分析目的，生成一系列OpenRefine操作，以最小化清理后的表格来满足目标。六种操作对应常见数据质量问题，包括格式不一致、类型错误和重复项。为评估性能，我们构建了一个包含142个分析目的的基准，覆盖96张表格和六个主题。评估涵盖三个维度：（1）目的回答：清理后的表格能否生成正确答案；（2）列（值）：与真实表的匹配程度；（3）工作流（操作）：生成流程与人工标注流程的相似度。实验表明，Llama 3.1、Mistral和Gemma 2显著提升数据质量，在所有指标上均优于基线。Gemma 2-27B始终生成高质量表格与答案，而Gemma 2-9B在生成接近人工标注的工作流方面表现最佳。
原文摘要 · Abstract (English)
Data cleaning is a time-consuming and error-prone manual process, even with modern workflow tools such as OpenRefine. We present AutoDCWorkflow, an LLM-based pipeline for automatically generating data-cleaning workflows. The pipeline takes a raw table and a data analysis purpose, and generates a sequence of OpenRefine operations designed to produce a minimal, clean table sufficient to address the purpose. Six operations correspond to common data quality issues, including format inconsistencies, type errors, and duplicates. To evaluate AutoDCWorkflow, we create a benchmark with metrics assessing answers, data, and workflow quality for 142 purposes using 96 tables across six topics. The evaluation covers three key dimensions: (1) Purpose Answer: can the cleaned table produce a correct answer? (2) Column (Value): how closely does it match the ground truth table? (3) Workflow (Operations): to what extent does the generated workflow resemble the human-curated ground truth? Experiments show that Llama 3.1, Mistral, and Gemma 2 significantly enhance data quality, outperforming the baseline across all metrics. Gemma 2-27B consistently generates high-quality tables and answers, while Gemma 2-9B excels in producing workflows that closely resemble human-annotated versions.
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