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PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation

Jinyu Wang, Jingjing Fu, Rui Wang, Lei Song, Jiang Bian

TL;DR

<3-5 sentence high-level summary> PIKE-RAG introduces a specialized knowledge and rationale augmentation framework that extends Retrieval-Augmented Generation with a multi-layer heterogeneous knowledge base, knowledge atomizing, and knowledge-aware task decomposition to handle four classes of industrial questions. It defines a phased development path (L0–L4) and demonstrates how iterative retrieval, structured knowledge, and multi-agent reasoning improve performance on open-domain multi-hop benchmarks and legal QA tasks. The approach includes detailed methodology, dataset collection for domain-aligned proposers, and real-case studies to validate the efficacy of knowledge-aware decomposition in complex settings. The findings provide a practical roadmap for deploying RAG systems in industry, balancing retrieval quality, reasoning depth, and efficiency through modular, extensible components.

Abstract

Despite notable advancements in Retrieval-Augmented Generation (RAG) systems that expand large language model (LLM) capabilities through external retrieval, these systems often struggle to meet the complex and diverse needs of real-world industrial applications. The reliance on retrieval alone proves insufficient for extracting deep, domain-specific knowledge performing in logical reasoning from specialized corpora. To address this, we introduce sPecIalized KnowledgE and Rationale Augmentation Generation (PIKE-RAG), focusing on extracting, understanding, and applying specialized knowledge, while constructing coherent rationale to incrementally steer LLMs toward accurate responses. Recognizing the diverse challenges of industrial tasks, we introduce a new paradigm that classifies tasks based on their complexity in knowledge extraction and application, allowing for a systematic evaluation of RAG systems' problem-solving capabilities. This strategic approach offers a roadmap for the phased development and enhancement of RAG systems, tailored to meet the evolving demands of industrial applications. Furthermore, we propose knowledge atomizing and knowledge-aware task decomposition to effectively extract multifaceted knowledge from the data chunks and iteratively construct the rationale based on original query and the accumulated knowledge, respectively, showcasing exceptional performance across various benchmarks.

PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation

TL;DR

<3-5 sentence high-level summary> PIKE-RAG introduces a specialized knowledge and rationale augmentation framework that extends Retrieval-Augmented Generation with a multi-layer heterogeneous knowledge base, knowledge atomizing, and knowledge-aware task decomposition to handle four classes of industrial questions. It defines a phased development path (L0–L4) and demonstrates how iterative retrieval, structured knowledge, and multi-agent reasoning improve performance on open-domain multi-hop benchmarks and legal QA tasks. The approach includes detailed methodology, dataset collection for domain-aligned proposers, and real-case studies to validate the efficacy of knowledge-aware decomposition in complex settings. The findings provide a practical roadmap for deploying RAG systems in industry, balancing retrieval quality, reasoning depth, and efficiency through modular, extensible components.

Abstract

Despite notable advancements in Retrieval-Augmented Generation (RAG) systems that expand large language model (LLM) capabilities through external retrieval, these systems often struggle to meet the complex and diverse needs of real-world industrial applications. The reliance on retrieval alone proves insufficient for extracting deep, domain-specific knowledge performing in logical reasoning from specialized corpora. To address this, we introduce sPecIalized KnowledgE and Rationale Augmentation Generation (PIKE-RAG), focusing on extracting, understanding, and applying specialized knowledge, while constructing coherent rationale to incrementally steer LLMs toward accurate responses. Recognizing the diverse challenges of industrial tasks, we introduce a new paradigm that classifies tasks based on their complexity in knowledge extraction and application, allowing for a systematic evaluation of RAG systems' problem-solving capabilities. This strategic approach offers a roadmap for the phased development and enhancement of RAG systems, tailored to meet the evolving demands of industrial applications. Furthermore, we propose knowledge atomizing and knowledge-aware task decomposition to effectively extract multifaceted knowledge from the data chunks and iteratively construct the rationale based on original query and the accumulated knowledge, respectively, showcasing exceptional performance across various benchmarks.
Paper Structure (49 sections, 18 figures, 11 tables, 3 algorithms)

This paper contains 49 sections, 18 figures, 11 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related work
  3. RAG
  4. Knowledge bases for RAG
  5. Multi-hop QA
  6. Problem formulation
  7. Knowledge base
  8. Task classification
  9. Factual Questions
  10. Linkable-Reasoning Questions
  11. Predictive Questions
  12. Creative Questions
  13. RAG system level
  14. Methodology
  15. Framework
  16. ...and 34 more sections

Figures (18)

  • Figure 1: Illustrative examples of distinct question types
  • Figure 2: Overview of the PIKE-RAG framework, comprising several key components: file parsing, knowledge extraction, knowledge storage, knowledge retrieval, knowledge organization, task decomposition and coordination, and knowledge-centric reasoning. Each component can be tailored to meet the evolving demands of system capability.
  • Figure 3: Multi-layer heterogeneous graph as the knowledge base. The graph comprises three distinct layers: information resource layer, corpus layer and distilled knowledge layer.
  • Figure 4: The process of distilling knowledge from corpus text. The corpus text are processed to extract knowledge units following customized extraction patterns. These knowledge units are then organized to structured knowledge in the distilled knowledge layer, which may take the form of knowledge graphs, atomic knowledge, tabular knowledge, and other induced knowledge.
  • Figure 5: Illustration of enhanced chunking with recurrent text splitting.
  • ...and 13 more figures