  Large language models have demonstrated robust performance on various
language tasks using zero-shot or few-shot learning paradigms. While being
actively researched, multimodal models that can additionally handle images as
input have yet to catch up in size and generality with language-only models. In
this work, we ask whether language-only models can be utilised for tasks that
require visual input -- but also, as we argue, often require a strong reasoning
component. Similar to some recent related work, we make visual information
accessible to the language model using separate verbalisation models.
Specifically, we investigate the performance of open-source, open-access
language models against GPT-3 on five vision-language tasks when given
textually-encoded visual information. Our results suggest that language models
are effective for solving vision-language tasks even with limited samples. This
approach also enhances the interpretability of a model's output by providing a
means of tracing the output back through the verbalised image content.
