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RAG vs KAG: A Comparative Analysis of Retrieval-Augmented Generation and Knowledge-Augmented Generation
PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation
PIKE-RAG is a novel Retrieval-Augmented Generation (RAG) framework designed to overcome limitations of existing RAG systems in industrial applications by focusing on specialized knowledge extraction and rationale construction. It introduces a task classification paradigm to systematically evaluate RAG capabilities and supports phased development. Key innovations include knowledge atomizing and knowledge-aware task decomposition, which enhance performance on complex queries. ✨
Article Points:
1
PIKE-RAG enhances RAG for industrial tasks via specialized knowledge & rationale.
2
New task classification paradigm evaluates RAG problem-solving capabilities systematically.
3
PIKE-RAG enables phased RAG system development and capability enhancement.
4
Knowledge atomizing extracts multifaceted knowledge from data chunks.
5
Knowledge-aware task decomposition constructs coherent rationale for LLMs.
6
Trainable decomposer integrates domain-specific rationale into task decomposition.
PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation
Challenges in RAG
Domain-specific knowledge deficit
Knowledge source diversity
One-size-fits-all approach
PIKE-RAG Solution
Specialized knowledge & rationale augmentation
Phased system development
Knowledge atomizing & task decomposition
Task Classification
Factual Questions
Linkable-Reasoning Questions
Predictive Questions
Creative Questions
System Development
L0: Knowledge Base Construction
L1: Factual Question RAG
L2: Linkable & Reasoning RAG
L3: Predictive Question RAG
L4: Creative Question RAG
Methodology
Multi-layer heterogeneous graph
Enhanced chunking & auto-tagging
Knowledge-aware task decomposition training
Evaluation