The use of entropy-based metrics in E-Sparse has profound implications on the interpretability of pruned models. By leveraging information entropy to quantify the amount of information within each channel of hidden state features, E-Sparse provides a more nuanced understanding of channel importance in LLMs. The incorporation of both fine-grained (entropy) and coarse-grained (amplitude) indicators enhances the evaluation metric for parameter importance, leading to more precise pruning decisions based on channel-specific characteristics. This approach not only improves efficiency but also offers insights into how different channels contribute to model performance, making pruned models more interpretable.