PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation

TL;DR

PIKE-RAG enhances retrieval-augmented generation by integrating specialized knowledge and rationale construction, improving logical reasoning and domain-specific understanding for complex industrial tasks.

Contribution

The paper introduces a novel framework that combines knowledge atomization, task decomposition, and rationale generation to improve RAG systems' performance on complex, domain-specific tasks.

Findings

Achieves superior performance on multiple benchmarks.

Effectively extracts multifaceted knowledge from data chunks.

Systematically evaluates RAG capabilities based on task complexity.

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.