Update utils.py

utils.py

@@ -31,25 +31,23 @@ LEADERBOARD_INTRODUCTION = """# MMLU-Pro Leaderboard
 
 Welcome to the MMLU-Pro leaderboard, showcasing the performance of various advanced language models on the MMLU-Pro dataset. The MMLU-Pro dataset is an enhanced version of the original MMLU, specifically engineered to offer a more rigorous and realistic evaluation environment..
 
-The MMLU-Pro dataset consists of approximately 12,000 intricate questions that challenge the comprehension and reasoning abilities of LLMs. Below you can find the accuracies of different models tested on this dataset.
+## What's new about MMLU-Pro
+
+Compared to the original MMLU, there are three major differences:
+
+- The original MMLU dataset only contains 4 options, MMLU-Pro increases it to 10 options. The increase in options will make the evaluation more realistic and challenging. The random guessing will lead to a much lower score.
+- The original MMLU dataset contains mostly knowledge-driven questions without requiring much reasoning. Therefore, PPL results are normally better than CoT. In our dataset, we increase the problem difficulty and integrate more reasoning-focused problems. In MMLU-Pro, CoT can be 20% higher than PPL. 
+- Due to the increase of options, we found that the model performance becomes more robust. For example, Llama-2-7B performance variance on MMLU-Pro is within 1% with several different prompts. In contrast, the performance variance on original MMLU can be as huge as 4-5%.
 
 For detailed information about the dataset, visit our page on Hugging Face: https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro. If you are interested in replicating these results or wish to evaluate your models using our dataset, access our evaluation scripts available on GitHub: https://github.com/TIGER-AI-Lab/MMLU-Pro.
+Below you can find the accuracies of different models tested on this dataset.
 """
 
 TABLE_INTRODUCTION = """
  """
 
 LEADERBOARD_INFO = """
-
-## 1. What's new about MMLU-Pro
-
-Compared to the original MMLU, there are three major differences:
-
-- The original MMLU dataset only contains 4 options, MMLU-Pro increases it to 10 options. The increase in options will make the evaluation more realistic and challenging. The random guessing will lead to a much lower score.
-- The original MMLU dataset contains mostly knowledge-driven questions without requiring much reasoning. Therefore, PPL results are normally better than CoT. In our dataset, we increase the problem difficulty and integrate more reasoning-focused problems. In MMLU-Pro, CoT can be 20% higher than PPL. 
-- Due to the increase of options, we found that the model performance becomes more robust. For example, Llama-2-7B performance variance on MMLU-Pro is within 1% with several different prompts. In contrast, the performance variance on original MMLU can be as huge as 4-5%.
-
-## 2. Dataset Summary
+## Dataset Summary
 
 - **Questions and Options:** Each question within the dataset typically has **ten** multiple-choice options, except for some that were reduced during the manual review process to remove unreasonable choices. This increase from the original **four** options per question is designed to enhance complexity and robustness, necessitating deeper reasoning to discern the correct answer among a larger pool of potential distractors.
  
@@ -59,7 +57,6 @@ Compared to the original MMLU, there are three major differences:
  - **TheoremQA:** High-quality human-annotated questions requiring theorems to solve.
  - **Scibench:** Science questions from college exams.
 
-
 """
 
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"