One of the main methods for semantic interpretation of text is mapping it
into a vector in some embedding space. Such vectors can then be used for a
variety of text processing tasks. Recently, most embedding spaces are a product
of training large language models. One major drawback of this type of
representation is its incomprehensibility to humans. Understanding the
embedding space is crucial for several important needs, including the need to
explain the decision of a system that uses the embedding, the need to debug the
embedding method and compare it to alternatives, and the need to detect biases
hidden in the model. In this paper, we present a novel method of transforming
any embedding space into a comprehensible conceptual space. We first present an
algorithm for deriving a conceptual space with dynamic on-demand granularity.
We then show a method for transferring any vector in the original
incomprehensible space to an understandable vector in the conceptual space. We
combine human tests with cross-model tests to show that the conceptualized
vectors indeed represent the semantics of the original vectors. We also show
how the conceptualized vectors can be used for various tasks including
identifying weaknesses in the semantics underlying the original spaces and
differences in the semantics of alternative models.

本文提出了一种将任何嵌入空间转换成易理解的概念空间的方法，并展示了该方法在语义表示方面的实用价值，如发现潜藏的偏见和比较不同模型间语义的差异。