  Multi-modal Large Language Models (MLLMs) have demonstrated remarkable
capabilities in understanding and generating content across various modalities,
such as images and text. However, their interpretability remains a challenge,
hindering their adoption in critical applications. This research proposes a
novel approach to enhance the interpretability of MLLMs by focusing on the
image embedding component. We combine an open-world localization model with a
MLLM, thus creating a new architecture able to simultaneously produce text and
object localization outputs from the same vision embedding. The proposed
architecture greatly promotes interpretability, enabling us to design a novel
saliency map to explain any output token, to identify model hallucinations, and
to assess model biases through semantic adversarial perturbations.
