From cbfaedac347b33c6014b7faaad8dcb1afb762193 Mon Sep 17 00:00:00 2001 From: maszhongming Date: Tue, 16 Sep 2025 15:22:06 -0500 Subject: Update README --- README.md | 56 +++++++++++++++++++++++++++++++++++--------------------- 1 file changed, 35 insertions(+), 21 deletions(-) (limited to 'README.md') diff --git a/README.md b/README.md index e6ed987..f718531 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -\

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# CS 598 JH Assignment: Enhancing KG-RAG for Biomedical Question Answering @@ -54,54 +54,68 @@ python data/my_results/evaluate_gemini.py ### **Step 5: Implement Enhancement Strategies** -This is the core of the assignment. You are required to implement **3 distinct improvement strategies** in the [**`kg_rag/rag_based_generation/GPT/run_mcq_qa.py`**](kg_rag/rag_based_generation/GPT/run_mcq_qa.py) file. +This is the core of the assignment. You are required to implement **3 distinct improvement strategies** in the [`kg_rag/rag_based_generation/GPT/run_mcq_qa.py`](kg_rag/rag_based_generation/GPT/run_mcq_qa.py) file. -We have left TODO sections for `Mode 1`, `Mode 2`, and `Mode 3` in the code as placeholders for your implementations. +We have left TODO sections for `MODE 1`, `MODE 2`, and `MODE 3` in the code as placeholders for your implementations. ### **Step 6: Evaluate Your Enhancements** Evaluate the performance of each of your proposed strategies. 1. Ensure your enhanced model variant saves its output to a new file path. -2. Open the evaluation script at [**`data/my_results/evaluate_gemini.py`**](data/my_results/evaluate_gemini.py) and modify the file path to point to your new results file. +2. Open the evaluation script at [`data/my_results/evaluate_gemini.py`](data/my_results/evaluate_gemini.py) and modify the file path to point to your new results file. 3. Run the script again and record the results for each of your three strategies. ----- ## What is KG-RAG? -KG-RAG is a task-agnostic framework that combines the explicit knowledge of a Knowledge Graph (KG) with the implicit knowledge of a Large Language Model (LLM). It empowers a general-purpose LLM by incorporating an optimized, domain-specific **'prompt-aware context'** extracted from a massive biomedical KG called [SPOKE](https://spoke.ucsf.edu/). +KG-RAG stands for Knowledge Graph-based Retrieval Augmented Generation. -The main feature of KG-RAG is that it extracts the minimal context sufficient to respond to the user prompt, making the information provided to the LLM both dense and highly relevant. +### Start by watching the video of KG-RAG -\