Update README.md

README.md

@@ -60,17 +60,13 @@ Hence, this dataset is designed to support research and development in biomedica
 3. Assessing retrieval capabilities of various RAG (Retrieval-Augmented Generation) frameworks
 4. Supporting research in biomedical ontologies and knowledge graphs
 
-# BiomixQA Dataset
-
-[Previous sections remain unchanged]
-
 ## Performance Analysis
 
 We conducted a comprehensive analysis of the performance of three Large Language Models (LLMs) - Llama-2-13b, GPT-3.5-Turbo (0613), and GPT-4 - on the BiomixQA dataset. We compared their performance using both a standard prompt-based approach and our novel Knowledge Graph Retrieval-Augmented Generation (KG-RAG) framework.
 
 ### Performance Summary
 
-Table 1: Performance (accuracy) of LLMs on BiomixQA datasets using prompt-based (zero-shot) and KG-RAG approaches (For more details refer [this](https://arxiv.org/abs/2311.17330) paper)
+Table 1: Performance (accuracy) of LLMs on BiomixQA datasets using prompt-based (zero-shot) and KG-RAG approaches (For more details, refer [this](https://arxiv.org/abs/2311.17330) paper)
 
 | Model | True/False Dataset |    | MCQ Dataset |    |
 |-------|-------------------:|---:|------------:|---:|