add description

app.py

@@ -42,3 +42,34 @@ styled_data = (
 
 
 st.dataframe(styled_data, use_container_width=True, height=800, hide_index=True)
+
+st.text("\n\n")
+st.markdown(
+    r"""
+    This leaderboard measures the **system-level performance and behavior of LLM judges**, and was created as part of the **[JuStRank paper](https://www.arxiv.org/abs/2412.09569)** from ACL 2025.
+    
+    Judges are sorted according to *Ranking Agreement* with humans, i.e., comparing how the judges rank different systems (generative models) relative to how humans rank those systems on [LMSys Arena](https://lmarena.ai/leaderboard/text/hard-prompts-english).
+    
+    We also compare judges in terms of the *Decisiveness* and *Bias* reflected in their judgment behaviors (refer to the paper for details).
+    
+    In our research we tested 10 **LLM judges** and 8 **reward models**, and asked them to score the [responses](https://huggingface.co/datasets/lmarena-ai/arena-hard-auto/tree/main/data/arena-hard-v0.1/model_answer) of 63 systems to the 500 questions from Arena Hard v0.1.
+    For each LLM judge we tried 4 different _realizations_, i.e., different prompt and scoring methods used with the LLM judge.
+    
+    In total, the judge ranking is derived from **[1.5 million raw judgment scores](https://huggingface.co/datasets/ibm-research/justrank_judge_scores)** (48 judge realizations X 63 target systems X 500 instances).
+
+    If you find this useful, please cite our work 🤗
+
+    ```bibtex
+    @inproceedings{gera2025justrank,
+        title={JuStRank: Benchmarking LLM Judges for System Ranking}, 
+        author={Gera, Ariel and Boni, Odellia and Perlitz, Yotam and Bar-Haim, Roy and Eden, Lilach and Yehudai, Asaf},
+        booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
+        month={july},
+        address={Vienna, Austria},
+        year={2025}
+        url={www.arxiv.org/abs/2412.09569}, 
+    }
+    ```
+    """
+)
+