SAGE: Segmenting and Grouping Data Effectively using Large Language Models
Name
pedrazapineros-ipedraza-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
1.53 MB
Format
Adobe PDF
Checksum (MD5)
79bb5af7ba60cedaf696f6b8b5a2d358
Author(s)
Pedraza Pineros, Isabella
Advisor(s)
Satyanarayan, Arvind
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
Grouping is a technique used to organize data into manageable pieces, reducing cognitive load and enabling users to focus on discovering higher-level insights and generating new questions. However, creating groups remains a challenge, often requiring users to have prior domain knowledge or an understanding of the underlying structure of the data. We introduce SAGE, a novel technique that leverages the knowledge base and pattern recognition abilities of large language models (LLMs) to segment and group data with domainawareness. We instantiate our technique through two structures: bins and highlights; bins are contiguous, non-overlapping ranges that segment a single field into groups; highlights are multi-field intersections of ranges that surface broader groups in the data. We integrate these structures into Olli, an open-source tool that converts data visualizations into accessible, keyboard-navigable textual formats to facilitate a study with 15 blind and low-vision (BLV) participants, recognizing them as experts in assessing agency. Through this study, we evaluate how SAGE impacts a user’s interpretation of data and visualizations, and find our technique provides a rich contextual framework for users to independently scaffold their initial sensemaking process.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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