One potential limitation of using instruction-following datasets like FABA-Instruct is the reliance on human-generated descriptions, which may introduce biases or inconsistencies in the annotations. To address this, it is essential to ensure the quality and consistency of the annotations through rigorous annotation guidelines, multiple annotator checks, and continuous feedback mechanisms. Additionally, the dataset may suffer from limited coverage of diverse facial expressions and actions, leading to potential biases in model training. To mitigate this limitation, data augmentation techniques can be employed to increase the diversity of facial expressions and actions in the dataset. Furthermore, incorporating transfer learning from related tasks and domains can help improve the generalizability of the models trained on instruction-following datasets.