This post is a brief summary about the paper that I read for my study and curiosity, so I shortly arrange the content of the paper, titled PIKE-RAG: sPecIalized KnowedgE and Rationale Augmented Generation (Wang et al. arXiv 2025), that I read and studied.
They split the level of RAG System using four type of question.
The following denotes that question classification for task classification
The following show the RAG system’s level for the above types of question.
The following denotes how to consist of main modules with sub-modules
The following denotes the level of RAG system to handle four type of question.
The below show the mult-layer, multi-granularity heterogeneous graph in their paper.
For detailed experiment and explanation, refer to the paper, titled PIKE-RAG: sPecIalized KnowedgE and Rationale Augmented Generation (Wang et al. arXiv 2025)
Note(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.
Reference
- Paper
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