We use two other datasets – Indian Movie Face database (IMFDB) (Setty et al., 2013) and FG-NET (Lanitis
et al., 2002) to fine-tune BiomedGPTbase for age prediction. There are 19,906 images with three classes
in IMFDB and 1,002 images with 62 classes in the FG-NET dataset. We perform this task using task-
aware input formats for both image classification and VQA. Specifically, we use the instructions “What
does the image describe?” and “What is the chronological age?”, respectively. An interesting observation is
that BiomedGPT can achieve 94.8% and 19.9% accuracy on these two datasets with the first and default
instruction, but fails to answer the question (0% accuracies) with the second and unseen instruction in the
pretraining. This suggests that even if the instruction “What does the image describe?” is not strongly
correlated with the age prediction task or other new classification tasks, we can still use it for general image
BiomedGPT