Knowledge Graphs & Trust in LLMs
All research points to hallucinations in Generative AI solutions being here to stay. That said, there are several ways to reduce the likelihood of these hiccups. Techniques like Retrieval-Augmented Generation (RAG) ensure responses are anchored in factual, context-specific information, while approaches like LLM-as-a-Judge introduce additional scrutiny. Smaller, domain-specific models can further minimise irrelevant outputs, whilst Chain-of-Thought Prompting encourages step-by-step reasoning for complex queries. Crucially, Human-in-the-Loop Oversight can help ensure errors are caught before they escalate.
However a new study highlights the pivotal role that Knowledge Graphs play in also addressing these challenges, offering a foundation for accuracy, explainability, and governance in enterprise AI.
What are Knowledge Graphs?
Knowledge Graphs (KGs) are structured representations of information where data points are connected by relationships, forming an interconnected network. Think of them as a map that links concepts, entities, and facts in a way that machines can understand and query efficiently. Unlike traditional databases, KGs provide context and meaning, making them ideal for navigating complex questions and ensuring that responses are both accurate and traceable.
The Study’s Findings
The study demonstrates that integrating Knowledge Graphs with LLMs significantly improves performance. Benchmarks revealed that pairing KGs with LLMs enhances accuracy by up to 4x compared to systems relying solely on SQL databases. This improvement stems from KGs’ ability to:
Anchor Responses in Governed Data: By connecting LLMs to validated and curated datasets, KGs reduce the likelihood of errors or fabrications. For example, the study showcased scenarios in insurance and financial services where specific queries about "active policies" or "loss ratios" were correctly resolved only when grounded in a KG.
Enhance Explainability: Responses generated with KGs are traceable, allowing users to understand the origin of information and the reasoning behind the answer. Tools used in the study provided a breakdown of how queries were structured and mapped back to the data source, increasing user trust.
Support Incremental Development: Enterprises can start small, building KGs around specific use cases, and scale as needs evolve. One example highlighted in the study was a phased approach in the retail sector, where KGs were initially applied to product catalogues and later expanded to customer interaction data.
Moreover, the researchers identified key challenges and solutions when integrating KGs with LLMs. For instance, they found that adapting ontologies to align with LLM query behaviours — even at the cost of traditional ontology best practices— significantly improved accuracy. This pragmatic approach enabled the system to better interpret user intent, especially for ambiguous or context-dependent questions.
The study also highlighted the importance of robust testing methodologies. By maintaining a comprehensive set of test cases, the researchers ensured that updates to the KG or LLM did not introduce regressions or inconsistencies. This iterative testing process was critical for maintaining the system’s reliability over time.
The Future of Trusted AI
The probabilistic nature of LLMs means that hallucinations are, in many ways, a feature rather than a bug. However, as new techniques and developments like Knowledge Graphs show, we are continually finding ways to harness this potential while building systems that businesses can trust.
As AI continues to shape the enterprise landscape, the need for trustworthy solutions will only grow. With innovations like Knowledge Graphs, we are building a future where AI not only performs but also earns trust. If you’re exploring how to make your AI systems more robust and reliable, let’s talk about how Knowledge Graphs can play a pivotal role.