Update src/about.py

src/about.py

@@ -42,25 +42,25 @@ Addressing the gaps in existing LLM evaluation frameworks, this benchmark is spe
     3. Naturally collected data (reflecting indigenous cultural nuances)
     
 ### Key Datasets in the Benchmark
-The benchmark integrates the following datasets to ensure a robust evaluation of Persian LLMs:
-**Translated Datasets**
-    • 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.
-**Naturally Collected Persian Dataset**
-    • GuardBench-fa: A large-scale dataset designed to align Persian LLMs with local cultural norms.
+> The benchmark integrates the following datasets to ensure a robust evaluation of Persian LLMs:
+> **Translated Datasets**
+>    • 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.
+> **Naturally Collected Persian Dataset**
+>    • 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.
+> 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.
 
     
 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.