Intelligence analysts perform sensemaking over collections of documents using various visual and analytic techniques to gain insights from large amounts of text. As data scales grow, our work explores how to leverage two AI technologies, large language models (LLMs) and knowledge graphs (KGs), in a visual text analysis tool, enhancing sensemaking and helping analysts keep pace. Collaborating with intelligence community experts, we developed a visual analytics system called VisPile. VisPile integrates an LLM and a KG into various UI functions that assist analysts in grouping documents into piles, performing sensemaking tasks like summarization and relationship mapping on piles, and validating LLM- and KG-generated evidence. Our paper describes the tool, as well as feedback received from six professional intelligence analysts that used VisPile to analyze a text document corpus.
@article{arxiv.2510.09605,
title = {VisPile: A Visual Analytics System for Analyzing Multiple Text Documents With Large Language Models and Knowledge Graphs},
author = {Adam Coscia and Alex Endert},
journal= {arXiv preprint arXiv:2510.09605},
year = {2025}
}
Comments
Accepted to HICSS 2026. 10 pages, 4 figures. For a demo video, see https://youtu.be/vY6SqkkNeMQ. For a live demo, visit https://adamcoscia.com/papers/vispile/demo/. The source code is available at https://github.com/AdamCoscia/VisPile