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KG-RAG: Bridging the Gap Between Knowledge and Creativity

Diego Sanmartin

TL;DR

The paper addresses the reliability gap in large language model agents when handling knowledge-intensive tasks by grounding reasoning in a dynamically constructed Knowledge Graph. It introduces KG-RAG, a three-stage pipeline (Storage, Retrieval via Chain of Explorations, and Answer Generation) that builds a homogeneous KG with triple hypernodes from unstructured text and uses CoE to perform granular KGQA. Experimental results on ComplexWebQuestions show reductions in hallucinations and a viable, if currently not leading, performance compared to vector-based RAG methods, highlighting the method's potential and areas for improvement. The work lays a foundation for more reliable knowledge-grounded LMA systems and points to hardware and data-quality improvements as avenues for practical deployment.

Abstract

Ensuring factual accuracy while maintaining the creative capabilities of Large Language Model Agents (LMAs) poses significant challenges in the development of intelligent agent systems. LMAs face prevalent issues such as information hallucinations, catastrophic forgetting, and limitations in processing long contexts when dealing with knowledge-intensive tasks. This paper introduces a KG-RAG (Knowledge Graph-Retrieval Augmented Generation) pipeline, a novel framework designed to enhance the knowledge capabilities of LMAs by integrating structured Knowledge Graphs (KGs) with the functionalities of LLMs, thereby significantly reducing the reliance on the latent knowledge of LLMs. The KG-RAG pipeline constructs a KG from unstructured text and then performs information retrieval over the newly created graph to perform KGQA (Knowledge Graph Question Answering). The retrieval methodology leverages a novel algorithm called Chain of Explorations (CoE) which benefits from LLMs reasoning to explore nodes and relationships within the KG sequentially. Preliminary experiments on the ComplexWebQuestions dataset demonstrate notable improvements in the reduction of hallucinated content and suggest a promising path toward developing intelligent systems adept at handling knowledge-intensive tasks.

KG-RAG: Bridging the Gap Between Knowledge and Creativity

TL;DR

The paper addresses the reliability gap in large language model agents when handling knowledge-intensive tasks by grounding reasoning in a dynamically constructed Knowledge Graph. It introduces KG-RAG, a three-stage pipeline (Storage, Retrieval via Chain of Explorations, and Answer Generation) that builds a homogeneous KG with triple hypernodes from unstructured text and uses CoE to perform granular KGQA. Experimental results on ComplexWebQuestions show reductions in hallucinations and a viable, if currently not leading, performance compared to vector-based RAG methods, highlighting the method's potential and areas for improvement. The work lays a foundation for more reliable knowledge-grounded LMA systems and points to hardware and data-quality improvements as avenues for practical deployment.

Abstract

Ensuring factual accuracy while maintaining the creative capabilities of Large Language Model Agents (LMAs) poses significant challenges in the development of intelligent agent systems. LMAs face prevalent issues such as information hallucinations, catastrophic forgetting, and limitations in processing long contexts when dealing with knowledge-intensive tasks. This paper introduces a KG-RAG (Knowledge Graph-Retrieval Augmented Generation) pipeline, a novel framework designed to enhance the knowledge capabilities of LMAs by integrating structured Knowledge Graphs (KGs) with the functionalities of LLMs, thereby significantly reducing the reliance on the latent knowledge of LLMs. The KG-RAG pipeline constructs a KG from unstructured text and then performs information retrieval over the newly created graph to perform KGQA (Knowledge Graph Question Answering). The retrieval methodology leverages a novel algorithm called Chain of Explorations (CoE) which benefits from LLMs reasoning to explore nodes and relationships within the KG sequentially. Preliminary experiments on the ComplexWebQuestions dataset demonstrate notable improvements in the reduction of hallucinated content and suggest a promising path toward developing intelligent systems adept at handling knowledge-intensive tasks.
Paper Structure (18 sections, 16 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 18 sections, 16 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. Storage
  5. Retrieval
  6. Answer Generation
  7. Methodology
  8. Storage
  9. Retrieval
  10. Answer Generation
  11. Experiments
  12. Experimental Setup
  13. ComplexWebQuestions Dataset
  14. Evaluation Metrics
  15. Results and Analysis
  16. ...and 3 more sections

Figures (5)

  • Figure 1: shows the three core components of an AI agent: perception, brain, and action. The brain component integrates LLMs for dynamic reasoning and decision-making, alongside KGs for structured knowledge and memory storage.
  • Figure 2: Illustrations of triple hypernode behaviors and representations. \ref{['fig:hyperobjects']}a) Represents the behavior of hyperobject (triple hypernode) which allows them to connect their internal components to other nodes. \ref{['fig:hyperobjects']}b) Illustrates our approach to storing triple hypernodes (the orange node) in a traditional KG database.
  • Figure 3: Diagram illustrating the components to perform KGQA over a KG using Chain of Explorations (CoE).
  • Figure 4: An illustrative scenario demonstrating the Chain of Explorations methodology on a complex query involving historical and personal data.
  • Figure 5: An example comparison of responses to a complex query by (a) LLM without RAG, (b) LLM with vector similarity search RAG, (c) LLM with KG-RAG