  While existing image-text alignment models reach high quality binary
assessments, they fall short of pinpointing the exact source of misalignment.
In this paper, we present a method to provide detailed textual and visual
explanation of detected misalignments between text-image pairs. We leverage
large language models and visual grounding models to automatically construct a
training set that holds plausible misaligned captions for a given image and
corresponding textual explanations and visual indicators. We also publish a new
human curated test set comprising ground-truth textual and visual misalignment
annotations. Empirical results show that fine-tuning vision language models on
our training set enables them to articulate misalignments and visually indicate
them within images, outperforming strong baselines both on the binary alignment
classification and the explanation generation tasks. Our method code and human
curated test set are available at: https://mismatch-quest.github.io/
