Before revision:  For a pair of models, we compute the similarity of the k-nearest neighbors of every embedded object , and visualize the results as Local Neighborhood Dominoes: small multiples that facilitate rapid comparisons. Using case studies, we illustrate the types of insights the Embedding Comparator reveals including how fine-tuning embeddings changes semantics, how language changes over time, and how training data differences affect two seemingly similar models  .
After revision:  For a pair of models, we compute the similarity of the k-nearest neighbors of every embedded object between a pair of spaces. Through case studies, we illustrate the types of insights the Embedding Comparator reveals including how fine-tuning embeddings changes semantics, how language changes over time, and how training data differences affect two seemingly similar models .