# LangLasso: Interactive Cluster Descriptions through LLM Explanation

arXiv: https://arxiv.org/abs/2601.10458 · HTML: https://mlsanity.com/paper/2601.10458/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Towards Natural Language Interfaces for Data Visualization: A Survey](https://mlsanity.com/paper/2109.03506/) (2021)
- [Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Models](https://mlsanity.com/paper/2302.02094/) (2023)
- [t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections](https://mlsanity.com/paper/2002.06910/) (2020)
- [Large Language Models Can Infer Psychological Dispositions of Social Media Users](https://mlsanity.com/paper/2309.08631/) (2023)
- [Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning](https://mlsanity.com/paper/1905.03911/) (2019)
- [Exploring the Capability of LLMs in Performing Low-Level Visual Analytic Tasks on SVG Data Visualizations](https://mlsanity.com/paper/2404.19097/) (2024)
- [Are LLMs ready for Visualization?](https://mlsanity.com/paper/2403.06158/) (2024)
- [Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning](https://mlsanity.com/paper/2410.12164/) (2024)
- [MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks](https://mlsanity.com/paper/2503.20089/) (2025)
- [A Scalable Approach to Clustering Embedding Projections](https://mlsanity.com/paper/2504.07285/) (2025)

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

- [Scalable Semantic Steering of Embedding Projections](https://mlsanity.com/paper/2607.03978/) (2026)

## Abstract (arXiv)

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 textitLangLasso, 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