NLI and Text Summarization. For the NLI fine-tuning, we configure the maximum training epoch at
200, establish a learning rate of 7e-5, and apply a weight decay of 0.01. The maximum length of the target
output is set at 30. For text summarization tasks, we implement variable learning rates of 1e-3, 5e-4, and
1e-4 for the MeQSum, Icliniq, and HealthCareMagic datasets, respectively, corresponding to a noise ratio of
0.2 each. We cap the maximum input text sequence length at 512 for each dataset, with a corresponding
output text sequence length of 128. We also set the training epoch at 300, apply a length penalty of 0.7,
and utilize a label smoothing factor of 0.1.
Image Captioning. The input image is resized to a resolution of 480 × 480. The maximum output text
lengths for IU-Xray, Peir Gross, and ROCO are set at 45, 20, and 30, respectively. The rationale behind the
selection of these specific output lengths is detailed in Section A.2. By default, we set the beam search size
to 10.
BiomedGPT