# Automatic Histograms: Leveraging Language Models for Text Dataset Exploration

arXiv: https://arxiv.org/abs/2402.14880 · HTML: https://mlsanity.com/paper/2402.14880/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [Visualizing and Measuring the Geometry of BERT](https://mlsanity.com/paper/1906.02715/) (2019)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://mlsanity.com/paper/2202.06935/) (2022)
- [What's In My Big Data?](https://mlsanity.com/paper/2310.20707/) (2023)
- [Goal-Driven Explainable Clustering via Language Descriptions](https://mlsanity.com/paper/2305.13749/) (2023)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [The Role of Interactive Visualization in Explaining (Large) NLP Models: from Data to Inference](https://mlsanity.com/paper/2301.04528/) (2023)
- [Evaluation of Synthetic Datasets for Conversational Recommender Systems](https://mlsanity.com/paper/2212.08167/) (2022)
- [Large Scale Qualitative Evaluation of Generative Image Model Outputs](https://mlsanity.com/paper/2301.04518/) (2023)

## Built on by (most cited first, 8 of 8)

- [Exploring Empty Spaces: Human-in-the-Loop Data Augmentation](https://mlsanity.com/paper/2410.01088/) (2024)
- [The Evolution of LLM Adoption in Industry Data Curation Practices](https://mlsanity.com/paper/2412.16089/) (2024)
- [Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap](https://mlsanity.com/paper/2512.04680/) (2025)
- [Reasoning-Driven Synthetic Data Generation and Evaluation](https://mlsanity.com/paper/2603.29791/) (2026)
- [Composable Prompting Workspaces for Creative Writing: Exploration and Iteration Using Dynamic Widgets](https://mlsanity.com/paper/2503.21394/) (2025)
- [PromptCanvas: Composable Prompting Workspaces Using Dynamic Widgets for Exploration and Iteration in Creative Writing](https://mlsanity.com/paper/2506.03741/) (2025)
- [Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework](https://mlsanity.com/paper/2508.12257/) (2025)
- [Visual Fingerprints for LLM Generation Comparison](https://mlsanity.com/paper/2605.06054/) (2026)

## Abstract (arXiv)

Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data workers often rely on dataset summaries, especially distributions of various derived features. Some features, like toxicity or topics, are relevant to many datasets, but many interesting features are domain specific: instruments and genres for a music dataset, or diseases and symptoms for a medical dataset. Accordingly, data workers often run custom analyses for each dataset, which is cumbersome and difficult. We present AutoHistograms, a visualization tool leveragingLLMs. AutoHistograms automatically identifies relevant features, visualizes them with histograms, and allows the user to interactively query the dataset for categories of entities and create new histograms. In a user study with 10 data workers (n=10), we observe that participants can quickly identify insights and explore the data using AutoHistograms, and conceptualize a broad range of applicable use cases. Together, this tool and user study contributeto the growing field of LLM-assisted sensemaking tools.