Update src/about.py

src/about.py

@@ -23,7 +23,7 @@ NUM_FEWSHOT = 0 # Change with your few shot
 
 
 # Your leaderboard name
-TITLE = """<h1 align="center" id="space-title">Demo leaderboard</h1>"""
+TITLE = """<h1 align="center" id="space-title">Open Persian LLM Alignment Leaderboard</h1>"""
 
 # What does your leaderboard evaluate?
 INTRODUCTION_TEXT = """
@@ -32,10 +32,33 @@ Intro text
 
 # Which evaluations are you running? how can people reproduce what you have?
 LLM_BENCHMARKS_TEXT = f"""
-## How it works
-
-## Reproducibility
-To reproduce our results, here is the commands you can run:
+## Open Persian LLM Alignment Leaderboard
+
+Developed by MCILAB in collaboration with the Machine Learning Laboratory at Sharif University of Technology, this benchmark presents a comprehensive evaluation framework for assessing the alignment of Persian Large Language Models (LLMs) with critical ethical dimensions, including safety, fairness, and social norms.
+Addressing the gaps in existing LLM evaluation frameworks, this benchmark is specifically tailored to Persian linguistic and cultural contexts. It combines three types of Persian-language benchmarks:
+    1. Translated datasets (adapted from established English benchmarks)
+    2. Synthetically generated data (newly created for Persian LLMs)
+    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.
+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.
+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.
 
 """