Detecting out of distribution queries poses a challenge, as neural networks often fail to recognize variations not present in their training data. The hope lies in leveraging large language models and image-based models that have encountered extensive variability, potentially allowing them to identify new objects distinctly. However, a crucial question remains: how can we map and address the missing variability in training data?
