Integrating Knowledge Graphs with Large Language Models for More Human-like AI Reasoning

Reasoning — the ability to think logically and make inferences from knowledge — is integral to human intelligence. As we progress towards developing artificial general intelligence, reasoning remains a core challenge for AI systems. While large language models (LLMs) like GPT-3 exhibit impressive reasoning capabilities, they lack the structured knowledge representations that support robust reasoning in humans. Knowledge graphs help overcome this limitation by encoding concepts and relations in an interconnected, machine-readable format. This article analyzes how combining LLMs with knowledge graphs can produce AI systems with more human-like reasoning proficiency. <h1>Limitations of Current AI Reasoning</h1> LLMs have achieved remarkable success across NLP tasks including dialogue, question answering, and summarization. However, current LLMs have certain limitations when it comes to complex reasoning <a href="https://pub.aimind.so/integrating-knowledge-graphs-with-large-language-models-for-more-human-like-ai-reasoning-55a5ba2988e2">Click Here</a>