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README.md

@@ -70,11 +70,13 @@ The 13B model is trained on 32 A100 GPUs. The learning rate (LR) is controlled b
 
 - **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.
+- **Commonsense Reasoning**: We report the average 0-shot accuracies on PIQA, SIQA, HellaSwag, WinoGrande, and COPA.
 
-- **Reading Comprehension**: We compute the average 0-shot PPL on BoolQ, 0-shot accuracy on LAMBADA and TyDi QA.
+- **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 the average PPL on AGI-Eval (0-shot).
+- **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.
+
+Note: 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.
 
 ### Evaluation Results