  The goal of vision-language modeling is to allow models to tie language
understanding with visual inputs. The aim of this paper is to evaluate and
align the Visual Language Model (VLM) called Multimodal Augmentation of
Generative Models through Adapter-based finetuning (MAGMA) with human values.
MAGMA is a VLM that is capable of image captioning and visual
question-answering. We will evaluate its alignment in three different
scenarios. To begin, we assess MAGMA's out-of-the-box alignment through the
checkpoint provided by Hugging Face. Then, we measure if few-shot learning
manages to improve the results. Finally, we finetune the model on aligned
examples and evaluate its behavior.
