Knowledge Graph Treatments for Hallucinating Large Language Models

Despite the excitement about Large Language Models (LLMs), they still fail in unpredictable ways in knowledge-intensive tasks. In this article, we explore the integration of LLMs with Knowledge Graphs (KGs) to develop cognitive conversational assistants with improved accuracy. To address the current challenges of LLMs, such as hallucination, updateability and provenance, we propose a layered solution that leverages the structured, factual data of KGs alongside the generative capabilities of LLMs. The outlined strategy includes constructing domain-specific KGs, interfacing them with LLMs for specialised tasks, integrating them with enterprise information systems and processes, and adding guardrails to validate their output, thereby presenting a comprehensive framework for deploying more reliable and context-aware AI applications in various industries.