Update src/about.py

src/about.py

@@ -45,25 +45,25 @@ Addressing the gaps in existing LLM evaluation frameworks, this benchmark is spe
 > The benchmark integrates the following datasets to ensure a robust evaluation of Persian LLMs:
 >
 > **Translated Datasets**
->    • Anthropic-fa
->    • AdvBench-fa
->    • HarmBench-fa
->    • DecodingTrust-fa
+>    - Anthropic-fa
+>    - AdvBench-fa
+>    - HarmBench-fa
+>    - DecodingTrust-fa
 >
 > **Newly Developed Persian Datasets**
->    • ProhibiBench-fa: Evaluates harmful and prohibited content in Persian culture.
->    • SafeBench-fa: Assesses safety in generated outputs.
->    • FairBench-fa: Measures bias mitigation in Persian LLMs.
->    • SocialBench-fa: Evaluates adherence to culturally accepted behaviors.
+>    - ProhibiBench-fa: Evaluates harmful and prohibited content in Persian culture.
+>    - SafeBench-fa: Assesses safety in generated outputs.
+>    - FairBench-fa: Measures bias mitigation in Persian LLMs.
+>    - SocialBench-fa: Evaluates adherence to culturally accepted behaviors.
 >
 > **Naturally Collected Persian Dataset**
->    • GuardBench-fa: A large-scale dataset designed to align Persian LLMs with local cultural norms.
+>    - GuardBench-fa: A large-scale dataset designed to align Persian LLMs with local cultural norms.
 
 ### A Unified Framework for Persian LLM Evaluation
  By combining these datasets, our work establishes a culturally grounded alignment evaluation framework, enabling systematic assessment across three key aspects:
-    • **Safety**: Avoiding harmful or toxic content.
-    • **Fairness**: Mitigating biases in model outputs.
-    • **Social Norms**: Ensuring culturally appropriate behavior.
+    - **Safety**: Avoiding harmful or toxic content.
+    - **Fairness**: Mitigating biases in model outputs.
+    - **Social Norms**: Ensuring culturally appropriate behavior.
 
     
 This benchmark not only fills a critical gap in Persian LLM evaluation but also provides a standardized leaderboard to track progress in developing aligned, ethical, and culturally aware Persian language models.