Uniting Large Language Models And Knowledge Graphs For Enhanced

TLDR The discussion centered around the use of knowledge graphs in combination with large language models, highlighting their ability to create explicit and implicit relationships and to overcome drawbacks of large language models by leveraging grounded factual knowledge. The conversation covered various techniques such as entity resolution, fine-tuning, few-shot learning, and retrieval augmented generation of graphs. The use of vectors for nodes and conducting vector searches for similarities and contextual understanding was emphasized, along with the availability of open-source tools and practical applications from NE forj.

Key Insights

Understanding Knowledge Graphs and Their Importance

The speaker introduces the concept of knowledge graphs, emphasizing the significance of entities and their relationships. They stress the importance of semantics and machine-readable semantics in defining knowledge graphs. Furthermore, the ability to learn from the network structure is highlighted, along with the detection of implicit relationships in factual knowledge.

Leveraging Graph Databases for Large Language Models

The discussion focuses on the creation of explicit relationships between users and the extraction of subgraphs for further analysis. Entity resolution for finding implicit relationships and the use of native graph databases like Neo4j to persist these relationships are mentioned. Moreover, the potential of combining knowledge graphs with language models for gaining better insights is emphasized.

Techniques for Refining Large Language Models Using Graphs

The conversation covers techniques such as fine-tuning, few-shot learning, and grounding the language model. Grounding involves using specific datasets based on factual knowledge to specialize the model. Additionally, retrieval augmented generation of graphs and the benefits of using knowledge graphs for grounding are explored, including accuracy, flexible schema, specificity, and role-based access control.

Utilizing Vectors for Semantic Searches within Knowledge Graphs

The speaker discusses the use of vectors as a powerful property for nodes and the ability to conduct vector searches for similarities and contextual understanding. They emphasize the scalability and production deployment of this technique, particularly in semantic searches within knowledge graphs. Additionally, they encourage exploring the NE forj sandbox, open-source tools, and materials available on their website for practical applications.

Questions & Answers

What is a knowledge graph?

A knowledge graph is defined as entities and their relationships, emphasizing the importance of semantics and machine-readable semantics.

How can knowledge graphs detect implicit relationships in factual knowledge?

Knowledge graphs can detect implicit relationships in factual knowledge by leveraging a native graph database like Neo4j to persist these implicit relationships.

What are the drawbacks of large language models, and how can they be addressed using graphs?

The drawbacks of large language models include hallucination and lack of control over input data. These issues can be addressed using techniques such as fine-tuning, few-shot learning, grounding the language model, and leveraging knowledge graphs for grounding, retrieval augmented generation, named entity recognition, and knowledge compression.

How are vectors used in knowledge graphs, and what are their benefits?

Vectors are used as a powerful property for nodes in knowledge graphs and enable conducting vector searches for similarities and contextual understanding. The benefits include scalability, production deployment, and semantic searches within knowledge graphs.