Recent studies applied Parameter Efficient Fine-Tuning techniques (PEFTs) to
efficiently narrow the performance gap between pre-training and downstream.
There are two important factors for various PEFTs, namely, the accessible data
size and fine-tunable parameter size. A natural expectation for PEFTs is that
the performance of various PEFTs is positively related to the data size and
fine-tunable parameter size. However, according to the evaluation of five PEFTs
on two downstream vision-language (VL) tasks, we find that such an intuition
holds only if the downstream data and task are not consistent with
pre-training. For downstream fine-tuning consistent with pre-training, data
size no longer affects the performance, while the influence of fine-tunable
parameter size is not monotonous. We believe such an observation could guide
the choice of training strategy for various PEFTs.

最近的研究应用了参数高效微调技术（PEFTs）来有效缩小预训练和下游任务之间的性能差距。该研究发现，对于与预训练一致的下游微调任务，数据规模不再影响性能，而可微参数规模的影响并不单调，这种观察可指导 PEFTs 的训练策略选择。