This has been addressed in7 prior work [4, 3] by finetuning VLMs on a given corpus for a given task [5] and8 conducting zero-shot evaluations on a new corpus [7]. However, the mere use of an9 unseen corpus for evaluation does not imply it is OOD. Q1 What do the instances that comprise the dataset represent (e.g., documents, photos,24 people,countries)? Pleaseprovideadescription.26 (a) We provide 384k image-text pairs. Q3 Does the dataset contain all possible instances or is it a sample (not necessarily ran-36 dom) of instances from a larger set? If the dataset is a sample, then what is the larger37 set?

Cross-modal (image-to-text and text-to-image) retrieval is an established task used in evaluation benchmarks to testthe performance of vision-language models (VLMs).

Second, pretraining with global, unfiltered data before fine-tuning on English content can improve cultural understanding without sacrificing performance on said popular benchmarks.

Despite their demonstrated utility for NLP, multimodal counterfactual examples have been relatively unexplored due to the difficulty of creating paired image-text data with minimal counterfactual changes. To address this challenge, we introduce a scalable framework for automatic generation of counterfactual examples using text-to-image diffusion models.

Several studies have attempted to reduce the number of image-text pairs used for vision-language pre-training.