Reliable data quality is crucial for downstream analysis of tabular datasets, yet rule-based validation often struggles with inefficiency, human intervention, and high computational costs. We present a three-stage framework that combines statistical inliner detection with LLM-driven rule and code generation. After filtering data samples through traditional clustering, we iteratively prompt LLMs to produce semantically valid quality rules and synthesize their executable validators through code-generating LLMs. To generate reliable quality rules, we aid LLMs with retrieval-augmented generation (RAG) by leveraging external knowledge sources and domain-specific few-shot examples. Robust guardrails ensure the accuracy and consistency of both rules and code snippets. Extensive evaluations on benchmark datasets confirm the effectiveness of our approach.
@article{arxiv.2509.10572,
title = {Quality Assessment of Tabular Data using Large Language Models and Code Generation},
author = {Ashlesha Akella and Akshar Kaul and Krishnasuri Narayanam and Sameep Mehta},
journal= {arXiv preprint arXiv:2509.10572},
year = {2025}
}