LangLasso: Interactive Cluster Descriptions through LLM Explanation

TL;DR

LangLasso leverages large language models to generate natural language descriptions of data clusters, enhancing interpretability and accessibility for non-experts in data analysis.

Contribution

It introduces a novel interactive method that uses LLMs to produce human-readable cluster explanations, bridging the gap between complex visual analytics and user-friendly interpretation.

Findings

LLMs can reliably generate meaningful cluster descriptions.

LangLasso improves accessibility for non-expert users.

The tool effectively engages broader audiences in data interpretation.

Abstract

Dimensionality reduction is a powerful technique for revealing structure and potential clusters in data. However, as the axes are complex, non-linear combinations of features, they often lack semantic interpretability. Existing visual analytics (VA) methods support cluster interpretation through feature comparison and interactive exploration, but they require technical expertise and intense human effort. We present \textit{LangLasso}, a novel method that complements VA approaches through interactive, natural language descriptions of clusters using large language models (LLMs). It produces human-readable descriptions that make cluster interpretation accessible to non-experts and allow integration of external contextual knowledge beyond the dataset. We systematically evaluate the reliability of these explanations and demonstrate that \langlasso provides an effective first step for engaging broader audiences in cluster interpretation. The tool is available at https://langlasso.vercel.app