# Quality Assessment of Tabular Data using Large Language Models and Code Generation

arXiv: https://arxiv.org/abs/2509.10572 · HTML: https://mlsanity.com/paper/2509.10572/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 67 references that are in the map.
- First posted on arXiv 11 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [HoloDetect: Few-Shot Learning for Error Detection](https://mlsanity.com/paper/1904.02285/) (2019)
- [Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets](https://mlsanity.com/paper/2503.06664/) (2025)
- [A Survey of Data Quality Measurement and Monitoring Tools](https://mlsanity.com/paper/1907.08138/) (2019)
- [Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference](https://mlsanity.com/paper/2006.04730/) (2020)
- [REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines](https://mlsanity.com/paper/2302.04702/) (2023)
- [Approximate Denial Constraints](https://mlsanity.com/paper/2005.08540/) (2020)
- [Sparx: Distributed Outlier Detection at Scale](https://mlsanity.com/paper/2206.01281/) (2022)
- [Automatic Data Repair: Are We Ready to Deploy?](https://mlsanity.com/paper/2310.00711/) (2023)
- [Towards Inconsistency Measurement in Business Rule Bases](https://mlsanity.com/paper/1911.08872/) (2019)
- [BClean: A Bayesian Data Cleaning System](https://mlsanity.com/paper/2311.06517/) (2023)
- [A Data Quality Metric (DQM): How to Estimate The Number of Undetected Errors in Data Sets](https://mlsanity.com/paper/1611.04878/) (2016)

## Built on by (most cited first, 1 of 1)

- [Constraint-Aware Synthetic Tabular Data Generation via Inter-Column Constraint Discovery with LLM Agents](https://mlsanity.com/paper/2608.15109/) (2026)

## Abstract (arXiv)

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.