Prompt tuning (Lester et al., 2021) is a simple yet effec-
tive parameter-efficient fine-tuning (PEFT) mechanism (Chen
et al., 2023a), which learns “soft prompts” or extra model pa-
rameters for each task instead of making a task-specific copy
of the entire pre-trained model for each downstream task and
inference must be performed in separate batches. We must
mention that the addition of soft prompts is contrary to the
design principle of the generalist model. In our experiments,
we inject two prompt layers into the encoder and decoder, re-
spectively following (Yang et al., 2022b), and vary the prompt
length {20, 40, 60, 80, 100} to investigate the performance com-
parison against full-model fine-tuning using BiomedGPTBase.
The accuracy of image classification on PathMNIST, Blood-
MNIST, and PneumoniaMNIST was obtained after 100, 512,
and 55 training epochs respectively, all with a consistent batch
size of 512.
Initial results in Figure 7 indicate that as the
BiomedGPT