Summary of Automatic Histograms: Leveraging Language Models For Text Dataset Exploration, by Emily Reif et al.
Automatic Histograms: Leveraging Language Models for Text Dataset Exploration
by Emily Reif, Crystal Qian, James Wexler, Minsuk Kahng
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces AutoHistograms, a visualization tool that leverages Large Language Models (LLMs) to help data workers make sense of unstructured text datasets. The tool automatically identifies relevant features, visualizes them with histograms, and allows users to interactively query the dataset for categories of entities and create new histograms. In a user study, participants were able to quickly identify insights and explore the data using AutoHistograms, and conceptualize a broad range of applicable use cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoHistograms is a tool that helps people understand big collections of text, like tweets or articles. It uses special computer models (Large Language Models) to find important things in the text, like topics or words about music or medicine. Then it shows these things in pictures called histograms. People can use AutoHistograms to look for patterns and themes in the text and get new ideas. |
