An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model

• URL: http://arxiv.org/abs/2403.08433v2
• Date: Sat, 18 May 2024 05:08:52 GMT
• Title: An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model
• Authors: Yuxin Tian, Mouxing Yang, Yunfan Li, Dayiheng Liu, Xingzhang Ren, Xi Peng, Jiancheng Lv,
• Abstract summary: A natural expectation for PEFTs is that the performance of various PEFTs is positively related to the data size and fine-tunable parameter size. 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.
• Score: 33.853380101736306
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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