Update src/assets/text_content.py

src/assets/text_content.py

@@ -6,6 +6,10 @@ INTRODUCTION_TEXT = f"""
 🐨 KG LLM Leaderboard aims to track, rank, and evaluate the performance of released Large Language Models on traditional KBQA/KGQA datasets.
 
 The data on this page is sourced from a research paper. If you intend to use the data from this page, please remember to cite the following source: https://arxiv.org/abs/2303.07992
+
+We compare the current SOTA traditional KBQA models (fine-tuned (FT) and zero-shot (ZS)),
+LLMs in the GPT family, and Other Non-GPT LLM. In QALD-9 and LC-quad2, the evaluation metric used is F1, while other datasets use Accuracy (Exact match).
+
 """
 
 LLM_BENCHMARKS_TEXT = f"""