Using large foundation models for data augmentation may have potential limitations or drawbacks that need to be considered. One limitation is the computational resources required to train and utilize these models effectively, as they can be computationally intensive and may require significant processing power and memory. Additionally, there may be challenges related to model interpretability and transparency, as large models can be complex and difficult to interpret, leading to potential issues with understanding how the augmented data is generated. Furthermore, there could be concerns about the generalization ability of the augmented data, as the models may introduce biases or artifacts that could impact the performance of the retrieval system. To address these limitations, it is important to carefully validate the augmented data, monitor for biases, and ensure that the augmentation process enhances the diversity and quality of the data without introducing unintended effects. Additionally, exploring techniques for model compression and optimization can help mitigate the computational challenges associated with large foundation models for data augmentation.