In this post, we’ll explore what a knowledge graph is and how it relates to large language models (LLMs). While the concept of knowledge graphs gained popularity around 2014 or 2015 and then plateaued, it has resurfaced strongly in 2023 alongside the rapid evolution of large models. The resurgence is primarily driven by one critical issue: hallucinations in LLMs.
The Problem of Hallucinations
Hallucination—the generation of fabricated or false information—is a significant obstacle to the practical implementation of large models. It undermines their reliability and limits their application in critical fields.
So, how can hallucinations be addressed? There are two main approaches:
- Improving Large Models: Future advancements in training methods may reduce hallucinations. However, current models still fall short of achieving the desired reliability.
- Confining Boundaries: By restricting models to operate within predefined boundaries and providing them with a rich set of factual data, we can test and leverage their understanding capabilities effectively. For example, a model tasked with organizing and summarizing 100 articles can do so far more efficiently than a human, provided it works within well-defined parameters.
Sources of Facts
To confine a model’s scope effectively, we need reliable sources of facts. These can include:
- Websites: Authoritative websites provide a wealth of information, though quality assurance measures are crucial.
- Company Documents: Internal documents are typically trustworthy, as they’re authored by knowledgeable personnel.
- Databases: Structured business-related data stored in systems like Oracle ensures authenticity.
- Knowledge Graphs: These structured data formats store facts using an entity-relationship model, offering unique advantages.
What Is a Knowledge Graph?
A knowledge graph is a structured way of storing information, referred to as “facts.” Unlike simple data repositories, it uses entities (real-world objects like people, locations, or companies) and relationships (connections between entities) to represent information.
For example:
- Lucas and Eric are entities.
- A company is another entity.
- Relationships could include “Lucas works at Company X” or “Eric previously worked at Company Y.”
This structure allows knowledge graphs to represent complex, domain-specific information—from medical and financial data to legal and risk-control contexts. Regardless of the domain, the essence of a knowledge graph lies in defining entities and their interconnections.
Advantages of Knowledge Graphs
Knowledge graphs offer several benefits over other data storage methods:
- Intuitive Representation: Relationships between entities are visually and structurally clear. For instance, finding all connections associated with Lucas (e.g., colleagues, employers, or projects) becomes straightforward.
- Key Information Extraction:
- From Documents: By focusing on relationships between entities, knowledge graphs capture the core ideas of a document.
- In Retrieval-Augmented Generation (RAG) Systems: When documents are converted into vectors, some entity-relationship information may be lost. Combining vectors with knowledge graphs ensures critical relationships are preserved, reducing hallucinations.
Complementary Use with Vector Databases
Knowledge graphs and vector databases often work in tandem. For example, Microsoft’s Graph RAG framework leverages the strengths of both to enhance data retrieval and minimize hallucinations. While vector databases represent text meaningfully, knowledge graphs ensure that essential entities and their relationships are not overlooked.
Challenges in Knowledge Graph Implementation
Despite their advantages, knowledge graphs face challenges:
- Graph Construction: Ensuring the accuracy of the constructed graph is critical.
- Integration: Seamlessly combining knowledge graphs with vector databases to capitalize on their strengths.
- Understanding Structured Data: Large models struggle with the inherently structured nature of knowledge graphs. Developing methods to teach models to interpret and utilize this structure is an ongoing challenge.
Conclusion
Knowledge graphs provide a powerful means of structuring and storing facts, making them invaluable in addressing hallucinations in large models. By integrating them with vector databases and improving their construction and usability, we can harness their potential to create more reliable and efficient AI systems. As research continues, knowledge graphs may become a cornerstone in the development of trustworthy AI solutions.
For detailed information, please watch our YouTube video: What Are Knowledge Graphs and How Do They Relate to LLMs?
