leads to a considerable boost in performance. In conclusion, by expanding the scale of data, tasks, and the
model, we foresee substantial enhancements in BiomedGPT’s few-shot and zero-shot inference capabilities.
Limitations and Suggestions.
limitations of
BiomedGPT. A primary concern is the model’s sensitivity toward instructions. There are instances where
the model fails to understand the instructions and makes catastrophic predictions, even producing irrelevant
data types such as image codes for a VQA task. A straightforward solution could be to broaden the diversity
of high-quality instruction sets during pretraining. Additionally, we must investigate methods for achieving
a balance in data diversity. This encompasses aspects such as establishing suitable size ratios for data within
different biomedical modalities in a batch and throughout the entire pretraining dataset and determining
BiomedGPT