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Mastering Gen AI: Tackling Challenges & Improving LLMs w/ Knowledge Graphs
Large Language Models (LLMs) are currently in high demand as they offer opportunities for task automation, enhanced customer experiences, and faster content retrieval, generation, and summarization. Nonetheless, several challenges, such as bias, inaccuracies, and hallucinations, need to be addressed. Additionally, the lack of domain-specific knowledge and the inability to validate and attribute sources hinder widespread adoption.
Conversely, Knowledge graphs have been in existence for over a decade and serve as the foundation for modern data and analytics platforms. They capture deep contextual relationships in data, eliminating the need to guess correlations. Knowledge graphs enable users to access all related information in one location, with comprehensive data relationships. Combining Knowledge graphs with LLMs, such as a Neo4j knowledge graph, enhances accuracy, explainability, and context in LLM-generated answers. Therefore, Knowledge graphs are an ideal complement to LLMs, ensuring accuracy and context in their outputs.
- Phani Dathar | Director of Graph Data Science @ Neo4j
- Himanshu Pandya | Associate Director, Global Analytics and Data Sciences @ Biogen
- Katie Roberts | Data Science Solution Architect @ Neo4j
- Sean Sanders | VP, Global Head of Data Science @ Randstad Sourceright