1. The comparison experiment with the MPOP method is lacking (See Missing References [1]). MPOP method is a lightweight fine-tuning method based on matrix decomposition and low-rank approximation, and it has a strong relevance to the method proposed in the paper. However, there is no comparison with this baseline method in the paper.
2. In Table 4, only the training time of the proposed method and AdaLoRA is compared, lacking the efficiency comparison with LoRA, Bitfit, and Adapter.
3. In the experimental section of the paper, the standard deviation after multiple experiments is not provided. The improvement brought by SoRA compared with the baseline is quite limited, which may be due to random fluctuations. The author should clarify which effects are within the range of standard deviation fluctuations and which are improvements brought by the SoRA method.
3. In the experimental section of the paper, the standard deviation after multiple experiments is not provided. The improvement brought by SoRA compared with the baseline is quite limited, which may be due to random fluctuations. The author should clarify which effects are within the range of standard deviation fluctuations and which are improvements brought by the SoRA method.