Data Wrangling Task Automation Using Code-Generating Language Models

• URL: http://arxiv.org/abs/2502.15732v1
• Date: Wed, 05 Feb 2025 03:36:29 GMT
• Title: Data Wrangling Task Automation Using Code-Generating Language Models
• Authors: Ashlesha Akella, Krishnasuri Narayanam,
• Abstract summary: We present an automated system that generates executable code for tasks like missing value imputation, error detection, and error correction.<n>Our system aims to identify inherent patterns in the data while leveraging external knowledge, effectively addressing both memory-dependent and memory-independent tasks.
• Score: 1.629488438606726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring data quality in large tabular datasets is a critical challenge, typically addressed through data wrangling tasks. Traditional statistical methods, though efficient, cannot often understand the semantic context and deep learning approaches are resource-intensive, requiring task and dataset-specific training. To overcome these shortcomings, we present an automated system that utilizes large language models to generate executable code for tasks like missing value imputation, error detection, and error correction. Our system aims to identify inherent patterns in the data while leveraging external knowledge, effectively addressing both memory-dependent and memory-independent tasks.

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