Authors: Juri Opitz, Lucas Möller, Andrianos Michail, Simon Clematide
Abstract: Text embeddings and text embedding models are a backbone of many AI and NLP
systems, particularly those involving search. However, interpretability
challenges persist, especially in explaining obtained similarity scores, which
is crucial for applications requiring transparency. In this paper, we give a
structured overview of interpretability methods specializing in explaining
those similarity scores, an emerging research area. We study the methods’
individual ideas and techniques, evaluating their potential for improving
interpretability of text embeddings and explaining predicted similarities.
Source: http://arxiv.org/abs/2502.14862v1
