  Vision and language model (VLM) decoders are currently the best-performing
architectures on multimodal tasks. Next to predictions, they can also produce
explanations, either in post-hoc or CoT settings. However, it is not clear how
much they use the vision and text modalities when generating predictions or
explanations. In this work, we investigate if VLMs rely on modalities
differently when they produce explanations as opposed to providing answers. We
also evaluate the self-consistency of VLM decoders in both post-hoc and CoT
explanation settings, by extending existing unimodal tests and measures to VLM
decoders. We find that VLMs are less self-consistent than LLMs. Text
contributions in VL decoders are more important than image contributions in all
examined tasks. Moreover, the contributions of images are significantly
stronger for explanation generation compared to answer generation. This
difference is even larger in CoT compared to post-hoc explanations. Lastly, we
provide an up-to-date benchmarking of state-of-the-art VL decoders on the VALSE
benchmark, which before only covered VL encoders. We find that VL decoders
still struggle with most phenomena tested by VALSE.
