# Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets

arXiv: https://arxiv.org/abs/2503.06664 · HTML: https://mlsanity.com/paper/2503.06664/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 20 of 20)

- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering](https://mlsanity.com/paper/2410.07095/) (2024)
- [DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation](https://mlsanity.com/paper/2211.11501/) (2022)
- [MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation](https://mlsanity.com/paper/2310.03302/) (2023)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery](https://mlsanity.com/paper/2410.05080/) (2024)
- [Can Foundation Models Wrangle Your Data?](https://mlsanity.com/paper/2205.09911/) (2022)
- [InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks](https://mlsanity.com/paper/2401.05507/) (2024)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [DiscoveryBench: Towards Data-Driven Discovery with Large Language Models](https://mlsanity.com/paper/2407.01725/) (2024)
- [HoloClean: Holistic Data Repairs with Probabilistic Inference](https://mlsanity.com/paper/1702.00820/) (2017)
- [DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models](https://mlsanity.com/paper/2410.07331/) (2024)
- [Rethinking Tabular Data Understanding with Large Language Models](https://mlsanity.com/paper/2312.16702/) (2023)
- [HoloDetect: Few-Shot Learning for Error Detection](https://mlsanity.com/paper/1904.02285/) (2019)
- [Benchmarking Data Science Agents](https://mlsanity.com/paper/2402.17168/) (2024)
- [AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark](https://mlsanity.com/paper/2412.06724/) (2024)
- [Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code](https://mlsanity.com/paper/2412.02764/) (2024)
- [CleanAgent: Automating Data Standardization with LLM-based Agents](https://mlsanity.com/paper/2403.08291/) (2024)
- [Large Language Models Engineer Too Many Simple Features For Tabular Data](https://mlsanity.com/paper/2410.17787/) (2024)
- [Automatic Data Repair: Are We Ready to Deploy?](https://mlsanity.com/paper/2310.00711/) (2023)

## Built on by (most cited first, 20 of 20)

- [A Survey of Data Agents: Emerging Paradigm or Overstated Hype?](https://mlsanity.com/paper/2510.23587/) (2025)
- [Large Language Model-based Data Science Agent: A Survey](https://mlsanity.com/paper/2508.02744/) (2025)
- [TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields](https://mlsanity.com/paper/2505.18125/) (2025)
- [Reinforcement Learning for Machine Learning Engineering Agents](https://mlsanity.com/paper/2509.01684/) (2025)
- [Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics](https://mlsanity.com/paper/2505.18658/) (2025)
- [Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning](https://mlsanity.com/paper/2509.02401/) (2025)
- [Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs](https://mlsanity.com/paper/2601.17058/) (2026)
- [A Survey of LLM × DATA](https://mlsanity.com/paper/2505.18458/) (2025)
- [DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science](https://mlsanity.com/paper/2602.24288/) (2026)
- [Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard](https://mlsanity.com/paper/2505.13421/) (2025)
- [Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection](https://mlsanity.com/paper/2512.07246/) (2025)
- [Matryoshka Agent: Unfolding Sub-Agents for Long-Horizon Machine Learning Engineering](https://mlsanity.com/paper/2607.25090/) (2026)
- [Quality Assessment of Tabular Data using Large Language Models and Code Generation](https://mlsanity.com/paper/2509.10572/) (2025)
- [Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data](https://mlsanity.com/paper/2509.21465/) (2025)
- [Cleaning Maintenance Logs with LLM Agents for Improved Predictive Maintenance](https://mlsanity.com/paper/2511.05311/) (2025)
- [ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows](https://mlsanity.com/paper/2605.12376/) (2026)
- [Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization](https://mlsanity.com/paper/2606.17915/) (2026)
- [Adaptive Graph-of-Islands Evolution for Automatic Feature Engineering with LLMs](https://mlsanity.com/paper/2607.23286/) (2026)
- [TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification](https://mlsanity.com/paper/2608.11044/) (2026)
- [Reward-rate Policy Gradient for Efficient Machine Learning Engineering Agents](https://mlsanity.com/paper/2609.36393/) (2026)

## Abstract (arXiv)

High-quality, error-free datasets are a key ingredient in building reliable, accurate, and unbiased machine learning (ML) models. However, real world datasets often suffer from errors due to sensor malfunctions, data entry mistakes, or improper data integration across multiple sources that can severely degrade model performance. Detecting and correcting these issues typically require tailor-made solutions and demand extensive domain expertise. Consequently, automation is challenging, rendering the process labor-intensive and tedious. In this study, we investigate whether Large Language Models (LLMs) can help alleviate the burden of manual data cleaning. We set up an experiment in which an LLM, paired with Python, is tasked with cleaning the training dataset to improve the performance of a learning algorithm without having the ability to modify the training pipeline or perform any feature engineering. We run this experiment on multiple Kaggle datasets that have been intentionally corrupted with errors. Our results show that LLMs can identify and correct erroneous entries, such as illogical values or outlier, by leveraging contextual information from other features within the same row, as well as feedback from previous iterations. However, they struggle to detect more complex errors that require understanding data distribution across multiple rows, such as trends and biases.