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.

该研究介绍了一个交互式系统 ——Embedding Comparator，它可以用于快速比较不同维度的嵌入向量空间，帮助用户快速可视化了解语义变化，实现了从手动操作到可视化操作的转换。