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@@ -108,7 +108,7 @@ where \\(\mathbf{s}\\), \\(\mathbf{x}\\), \\(\mathbf{x}_1\\), and \\(\odot\\) de
 
 The acceleration effects of LLMs with different sparsity are displayed as follows. ProSparse, which reaches a high sparsity without performance degradation, can gain the most benefits among all the settings concerned. Refer to Section 4.3 of [paper](TODO) for more details.
 
-|        Setting        | Average<br>Sparsity | Activation<br>Recall | Predicted<br>Sparsity | PowerInfer<br>Speed | `S2`<br>Time \\(\downarrow\\) | `S2`<br>Speedup | `S3`<br/>Time (\\(\downarrow\\)) | `S3`<br/>Speedup |
+|        Setting        | Average<br>Sparsity | Activation<br>Recall | Predicted<br>Sparsity | PowerInfer<br>Speed | `S2`<br>Time | `S2`<br>Speedup | `S3`<br/>Time | `S3`<br/>Speedup |
 | :-------------------: | :-----------------: | :------------------: | :-------------------: | :-----------------: | :--------------: | :-----------------: | :---------------: | :------------------: |
 |     ReluLLaMA-7B      |        66.98        |        90.89         |         58.95         |        11.37        |      67.12       |        1.35         |       63.00       |         1.32         |
 |    Vanilla ReLU-7B    |        66.04        |        87.72         |         72.57         |        12.04        |      67.85       |        1.33         |       63.28       |         1.31         |