On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs

TL;DR

This paper investigates the reasoning capabilities of large language models with respect to their internal knowledge graphs, focusing on recall accuracy, inference ability, and hallucination types during reasoning tasks.

Contribution

It introduces a systematic analysis of LLMs' reasoning with internal knowledge graphs and identifies hallucination types affecting reasoning accuracy.

Findings

LLMs can recall knowledge graph information accurately.

LLMs can infer relations from context effectively.

Two hallucination types impact reasoning quality.

Abstract

This paper examines the capacity of LLMs to reason with knowledge graphs using their internal knowledge graph, i.e., the knowledge graph they learned during pre-training. Two research questions are formulated to investigate the accuracy of LLMs in recalling information from pre-training knowledge graphs and their ability to infer knowledge graph relations from context. To address these questions, we employ LLMs to perform four distinct knowledge graph reasoning tasks. Furthermore, we identify two types of hallucinations that may occur during knowledge reasoning with LLMs: content and ontology hallucination. Our experimental results demonstrate that LLMs can successfully tackle both simple and complex knowledge graph reasoning tasks from their own memory, as well as infer from input context.