KG-RAG is a groundbreaking technology that combines the power of Knowledge Graph (KG) with Large Language Models (LLMs) to enhance text generation in the biomedical field. Developed by BaranziniLab, KG-RAG enables robust and context-rich generation of biomedical text.
With KG-RAG, GPT-like models are empowered by incorporating explicit knowledge from a Knowledge Graph. This fusion of KG and LLMs allows for more accurate and comprehensive text generation, particularly in the biomedical domain.
One of the notable achievements of KG-RAG is its 71% boost in performance for Llama2 and improved GPT models on biomedical datasets. This significant improvement demonstrates the effectiveness of KG-RAG in generating high-quality and context-aware text.
To learn more about KG-RAG and its capabilities, visit the official GitHub repository: KG-RAG. The repository provides detailed information on how to utilize KG-RAG, including installation instructions and usage examples.
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