Hon Kwan Shun Quinson
Hon Kwan Shun Quinson is an MPhil student in Computer Science at The Chinese University of Hong Kong. His academic pursuits and research focus on advancements in artificial intelligence. Throughout his academic journey, he has developed strong skills in natural language processing and machine learning. Notable experiences include developing graph-based Retrieval Augmented Generation (RAG) systems, training convolutional and Transformer models with PyTorch, and fine-tuning LLMs for applications such as Cantonese translation. He is also keenly expanding his research into the intersection of LLMs with reinforcement learning and formal methods for program synthesis, exploring novel pathways for enhancing AI capabilities and robustness. Committed to continuous growth in this rapidly evolving field, he is passionate about contributing to the future of AI.
Hong Kong SAR China
Session
Large Language Models (LLMs) often struggle to provide current and comprehensive answers from vast, interconnected knowledge bases, a common challenge in fields like business, legal and administrative tasks. While traditional RAG improves LLM context, it can falter with complex, relationship-heavy information. GraphRAG offers a powerful solution by leveraging graph databases to enhance retrieval with structured relationships, leading to deeper contextual understanding.
This talk provides a practical introduction to implementing GraphRAG using Neo4j. We will explore how Neo4j can be used to construct knowledge graphs from unstructured data and enable advanced, relationship-aware retrieval. Attendees will learn the core concepts of GraphRAG and gain practical insights to build smarter RAG systems, capable of delivering more accurate and contextually rich LLM responses for complex real-world applications.