Title: Conjuring Semantic Similarity
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Meaning Representation, Semantic Similarity, Diffusion Model

Abstract: The semantic similarity between sample expressions measures the distance between their latent `meaning'.These meanings are themselves typically represented by textual expressions.  We propose a novel approach whereby the semantic similarity among textual expressions is based not on other expressions they can be rephrased as, but rather based on the imagery they evoke. While this is not possible with humans, generative models allow us to easily visualize and compare generated images, or their distribution, evoked by a textual prompt. Therefore, we characterize the semantic similarity between two textual expressions simply as the distance between image distributions they induce, or 'conjure.' We show that by choosing the Jensen-Shannon divergence between the reverse-time diffusion stochastic differential equations (SDEs) induced by each textual expression, this can be directly computed via Monte-Carlo sampling. Our method contributes a novel perspective on semantic similarity that not only aligns with human-annotated scores, but also opens up new avenues for the evaluation of text-conditioned generative models while offering better interpretability of their learnt representations.

TL;DR: We propose a method to quantify, visualize, and measure the alignment of, semantic representations learned by text-to-image diffusion models.