AnchorTextVis: A Visual Analytics Approach for Fast Comparison of Text Embeddings.

Abstract

Visual comparison of text embeddings is crucial for analyzing semantic differences and comparing embedding models. Existing methods fail to maintain visual consistency in comparative regions and lack AI-assisted analysis, leading to high cognitive loads and time-consuming exploration processes. In this paper, we propose AnchorTextVis, a visual analytics approach based on AnchorMap-our dynamic projection algorithm balancing spatial quality and temporal coherence and LLMs to preserve users' mental map and accelerate the exploration process. We introduce the use of comparable dimensionality reduction algorithms that maintain visual consistency, such as AnchorMap from our previous work and Joint t-SNE. Building on this foundation, we leverage LLMs to compare and summarize, offering users insights. For quantitative comparisons, we define two complementary metrics, Shared KNN and Coordinate distance. Besides, we have also designed intuitive representation and rich interactive tools to compare clusters of texts and individual texts. We demonstrate the effectiveness and usefulness of our approach through three case studies and expert feedback.