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README.md

@@ -66,16 +66,6 @@ The 7B model is trained on 8 A100 GPUs. The learning rate (LR) is controlled by
 |        4        |   \\(5e-1\\)    | 16,000 |         33.55          |
 |        5        |   \\(5e-1\\)    | 16,500 |         34.60          |
 
-### Evaluation Benckmarks
-
-- **Code Generation**: We compute the average pass@1 scores on HumanEval (0-shot) and MBPP (3-shot).
-
-- **Commonsense Reasoning**: We report the average 0-shot perplexity (PPL) on PIQA, SIQA, HellaSwag, WinoGrande, and COPA.
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-- **Reading Comprehension**: We compute the average 0-shot PPL on BoolQ, 0-shot accuracy on LAMBADA and TyDi QA.
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-- **Other Popular Benchmarks**: We report the average accuracies on GSM8K (8-shot), MMLU (5-shot), Big Bench Hard (BBH) (3-shot), and the average PPL on AGI-Eval (0-shot).
-
 ### Evaluation Results
 
 The evaluation results on the above benchmarks demonstrate the advantage of ProSparse, which is the only method achieving high sparsity and comparable performance to the original Swish-activated LLaMA2. Note that models under all settings are trained with the same number of tokens on the same mixed dataset. Our evaluation is based on the framework [UltraEval](https://github.com/OpenBMB/UltraEval). The evaluation details are listed as follows:
@@ -86,7 +76,7 @@ The evaluation results on the above benchmarks demonstrate the advantage of ProS
 
 - **Reading Comprehension**: We compute the average 0-shot accuracies on BoolQ, 0-shot accuracy on LAMBADA and TyDi QA.
 
-- **Other Popular Benchmarks**: We report the average accuracies on GSM8K (8-shot), MMLU (5-shot), Big Bench Hard (BBH) (3-shot), and AGI-Eval (0-shot). Refer to Appendix~\ref{sec:eval-details} for more details.
+- **Other Popular Benchmarks**: We report the average accuracies on GSM8K (8-shot), MMLU (5-shot), Big Bench Hard (BBH) (3-shot), and AGI-Eval (0-shot).
 
 **Notes**: For PIQA, SIQA, HellaSwag, WinoGrande, COPA, BoolQ, LAMBADA, TyDi QA, and AGI-Eval, we obtain the predicted answers based on maximized perplexity. For GSM8K, MMLU, and BBH, the predicted answers are directly generated.