# Cocoon: Semantic Table Profiling Using Large Language Models

arXiv: https://arxiv.org/abs/2404.12552 · HTML: https://mlsanity.com/paper/2404.12552/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2024.

## Built on by (most cited first, 6 of 6)

- [A Survey of Data Agents: Emerging Paradigm or Overstated Hype?](https://mlsanity.com/paper/2510.23587/) (2025)
- [Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs](https://mlsanity.com/paper/2601.17058/) (2026)
- [PrepBench: How Far Are We from Natural-Language-Driven Data Preparation?](https://mlsanity.com/paper/2605.08687/) (2026)
- [CITBench: A Comprehensive Benchmark for Interactive Tabular Data Processing with LLMs](https://mlsanity.com/paper/2608.00018/) (2026)
- [Agentic Data Cleaning Without a Clean Reference: An Experimental Study of Capabilities and Trade-offs](https://mlsanity.com/paper/2608.14765/) (2026)
- [Towards Reliable AI Data Scientists: Data Agents with Workflow Harnesses](https://mlsanity.com/paper/2609.35255/) (2026)

## Abstract (arXiv)

Data profilers play a crucial role in the preprocessing phase of data analysis by identifying quality issues such as missing, extreme, or erroneous values. Traditionally, profilers have relied solely on statistical methods, which lead to high false positives and false negatives. For example, they may incorrectly flag missing values where such absences are expected and normal based on the data's semantic context. To address these, we introduce Cocoon, a data profiling system that integrates LLMs to imbue statistical profiling with semantics. Cocoon enhances traditional profiling methods by adding a three-step process: Semantic Context, Semantic Profile, and Semantic Review. Our user studies show that Cocoon is highly effective at accurately discerning whether anomalies are genuine errors requiring correction or acceptable variations based on the semantics for real-world datasets.