# Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples

arXiv: https://arxiv.org/abs/1912.04853 · HTML: https://mlsanity.com/paper/1912.04853/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 30 of 38)

- [AttentionViz: A Global View of Transformer Attention](https://mlsanity.com/paper/2305.03210/) (2023)
- [Shared Global and Local Geometry of Language Model Embeddings](https://mlsanity.com/paper/2503.21073/) (2025)
- [LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models](https://mlsanity.com/paper/2402.10524/) (2024)
- [WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings](https://mlsanity.com/paper/2306.09328/) (2023)
- [Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces](https://mlsanity.com/paper/2202.02641/) (2022)
- [One Wide Feedforward is All You Need](https://mlsanity.com/paper/2309.01826/) (2023)
- [CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM](https://mlsanity.com/paper/2408.05526/) (2024)
- [Visual Comparison of Language Model Adaptation](https://mlsanity.com/paper/2208.08176/) (2022)
- [Towards an Explainable Comparison and Alignment of Feature Embeddings](https://mlsanity.com/paper/2506.06231/) (2025)
- [Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs](https://mlsanity.com/paper/2102.08540/) (2021)
- [The Role of Interactive Visualization in Explaining (Large) NLP Models: from Data to Inference](https://mlsanity.com/paper/2301.04528/) (2023)
- [SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings](https://mlsanity.com/paper/2301.04704/) (2023)
- [Class-constrained t-SNE: Combining Data Features and Class Probabilities](https://mlsanity.com/paper/2308.13837/) (2023)
- [Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition](https://mlsanity.com/paper/2402.02617/) (2024)
- [Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationships](https://mlsanity.com/paper/2407.12543/) (2024)
- [VideoMap: Supporting Video Editing Exploration, Brainstorming, and Prototyping in the Latent Space](https://mlsanity.com/paper/2211.12492/) (2022)
- [A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations](https://mlsanity.com/paper/2407.17876/) (2024)
- [Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments](https://mlsanity.com/paper/2408.03274/) (2024)
- [Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Making](https://mlsanity.com/paper/2505.18066/) (2025)
- [Explainable Mapper: Charting LLM Embedding Spaces Using Perturbation-Based Explanation and Verification Agents](https://mlsanity.com/paper/2507.18607/) (2025)
- [DiffusionWorldViewer: Exposing and Broadening the Worldview Reflected by Generative Text-to-Image Models](https://mlsanity.com/paper/2309.09944/) (2023)
- [Latent Space Explorer: Visual Analytics for Multimodal Latent Space Exploration](https://mlsanity.com/paper/2312.00857/) (2023)
- [EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding](https://mlsanity.com/paper/2308.01329/) (2023)
- [k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood Analysis](https://mlsanity.com/paper/2312.04024/) (2023)
- [On the verification of Embeddings using Hybrid Markov Logic](https://mlsanity.com/paper/2312.08287/) (2023)
- [Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Making](https://mlsanity.com/paper/2405.16424/) (2024)
- [LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections](https://mlsanity.com/paper/2504.10504/) (2025)
- [RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systems](https://mlsanity.com/paper/2601.12991/) (2026)
- ["Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches](https://mlsanity.com/paper/2206.07918/) (2022)
- [Linking Robustness and Generalization: A k* Distribution Analysis of Concept Clustering in Latent Space for Vision Models](https://mlsanity.com/paper/2408.09065/) (2024)

## Abstract (arXiv)

Embeddings mapping high-dimensional discrete input to lower-dimensional continuous vector spaces have been widely adopted in machine learning applications as a way to capture domain semantics. Interviewing 13 embedding users across disciplines, we find comparing embeddings is a key task for deployment or downstream analysis but unfolds in a tedious fashion that poorly supports systematic exploration. In response, we present the Embedding Comparator, an interactive system that presents a global comparison of embedding spaces alongside fine-grained inspection of local neighborhoods. It systematically surfaces points of comparison by computing the similarity of the k-nearest neighbors of every embedded object between a pair of spaces. Through case studies across multiple modalities, we demonstrate our system rapidly reveals insights, such as semantic changes following fine-tuning, language changes over time, and differences between seemingly similar models. In evaluations with 15 participants, we find our system accelerates comparisons by shifting from laborious manual specification to browsing and manipulating visualizations.