Enhancing Next-Generation Language Models with Knowledge Graphs: Extending Claude, Mistral IA, and GPT-4 via KG-BERT

TL;DR

This paper proposes integrating Knowledge Graphs with large language models like Claude, Mistral IA, and GPT-4 using KG-BERT to improve factual accuracy and reasoning in knowledge-intensive NLP tasks.

Contribution

It introduces a novel method of combining KGs with LLMs via KG-BERT, enhancing their grounding and factual reliability.

Findings

Significant improvements in question answering accuracy

Enhanced entity linking performance

Better factual consistency in generated responses

Abstract

Large language models (LLMs) like Claude, Mistral IA, and GPT-4 excel in NLP but lack structured knowledge, leading to factual inconsistencies. We address this by integrating Knowledge Graphs (KGs) via KG-BERT to enhance grounding and reasoning. Experiments show significant gains in knowledge-intensive tasks such as question answering and entity linking. This approach improves factual reliability and enables more context-aware next-generation LLMs.