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Pyramid-Driven Alignment: Pyramid Principle Guided Integration of Large Language Models and Knowledge Graphs

Lei Sun, Xinchen Wang, Youdi Li

TL;DR

Pyramid-Driven Alignment (PDA), a novel framework for seamlessly integrating LLMs with KGs, utilizes Pyramid Principle analysis to construct a hierarchical pyramid structure designed to reflect the input question and generate more validated deductive knowledge, thereby enhancing the alignment of LLMs and KGs and ensuring more cohesive integration.

Abstract

Large Language Models (LLMs) possess impressive reasoning abilities but are prone to generating incorrect information, often referred to as hallucinations. While incorporating external Knowledge Graphs (KGs) can partially mitigate this issue, existing methods primarily treat KGs as static knowledge repositories, overlooking the critical disparity between KG and LLM knowledge, and failing to fully exploit the reasoning capabilities inherent in KGs. To address these limitations, we propose Pyramid-Driven Alignment (PDA), a novel framework for seamlessly integrating LLMs with KGs. PDA utilizes Pyramid Principle analysis to construct a hierarchical pyramid structure. This structure is designed to reflect the input question and generate more validated deductive knowledge, thereby enhancing the alignment of LLMs and KGs and ensuring more cohesive integration. Furthermore, PDA employs a recursive mechanism to harness the underlying reasoning abilities of KGs, resulting in more accurate knowledge retrieval for question-answering tasks. Our experimental results reveal a substantial performance advantage of PDA over state-of-the-art baselines, with improvements reaching 26.70% and 26.78%.

Pyramid-Driven Alignment: Pyramid Principle Guided Integration of Large Language Models and Knowledge Graphs

TL;DR

Pyramid-Driven Alignment (PDA), a novel framework for seamlessly integrating LLMs with KGs, utilizes Pyramid Principle analysis to construct a hierarchical pyramid structure designed to reflect the input question and generate more validated deductive knowledge, thereby enhancing the alignment of LLMs and KGs and ensuring more cohesive integration.

Abstract

Large Language Models (LLMs) possess impressive reasoning abilities but are prone to generating incorrect information, often referred to as hallucinations. While incorporating external Knowledge Graphs (KGs) can partially mitigate this issue, existing methods primarily treat KGs as static knowledge repositories, overlooking the critical disparity between KG and LLM knowledge, and failing to fully exploit the reasoning capabilities inherent in KGs. To address these limitations, we propose Pyramid-Driven Alignment (PDA), a novel framework for seamlessly integrating LLMs with KGs. PDA utilizes Pyramid Principle analysis to construct a hierarchical pyramid structure. This structure is designed to reflect the input question and generate more validated deductive knowledge, thereby enhancing the alignment of LLMs and KGs and ensuring more cohesive integration. Furthermore, PDA employs a recursive mechanism to harness the underlying reasoning abilities of KGs, resulting in more accurate knowledge retrieval for question-answering tasks. Our experimental results reveal a substantial performance advantage of PDA over state-of-the-art baselines, with improvements reaching 26.70% and 26.78%.

Paper Structure

This paper contains 20 sections, 8 equations, 2 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Methods
  3. Pyramid Alignment
  4. KG Reasoning
  5. Experiments
  6. Dataset
  7. Setup
  8. Baseline Models
  9. Main Results
  10. Discussion
  11. Effect of Pyramid Alignment and KG Reasoning
  12. Performance across Different Backbone Models
  13. Related Works
  14. Reasoning with LLM Prompting
  15. KG-Augmented LLM
  16. ...and 5 more sections

Figures (2)

  • Figure 1: An issue of the knowledge generated in LLMs reasoning and how it leads to irrelevant knowledge retrieval and subsequent errors in the final answer.
  • Figure 2: The overall framework of $\textsc{PDA}$. Pyramid Alignment: given a question, we apply the 5W1H framework to reflect it and construct a hierarchical pyramid that generates deductive knowledge. This deductive knowledge facilitates the alignment between LLM and KG. KG Reasoning: to utilizing the alignment knowledge, we leverage the inherent reasoning capabilities of KG to retrieve more accurate triples for answering the given question. Finally, we prompt LLM to generate the answer using these retrieved triples.