Cocoon: Semantic Table Profiling Using Large Language Models

TL;DR

Cocoon leverages large language models to improve data profiling by incorporating semantic understanding, reducing false positives and negatives in identifying data quality issues during preprocessing.

Contribution

We introduce Cocoon, a novel data profiling system that integrates LLMs to add semantic context to traditional statistical profiling methods.

Findings

Cocoon significantly reduces false positives in anomaly detection.

User studies demonstrate Cocoon's high accuracy in semantic anomaly assessment.

Cocoon effectively distinguishes between errors and acceptable data variations.

Abstract

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.