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.

翻译：尽管检索增强生成（RAG）系统通过外部检索扩展了大语言模型（LLM）的能力，并取得了显著进展，但这些系统往往难以满足现实工业应用中复杂多样的需求。仅依赖检索被证明不足以从专业语料库中提取深层、领域特定的知识并进行逻辑推理。为此，我们提出了基于专业知识与推理增强的生成（PIKE-RAG），其专注于提取、理解与应用专业知识，同时构建连贯的推理链，以逐步引导大语言模型生成准确响应。认识到工业任务面临的多样化挑战，我们引入了一种新范式，该范式根据任务在知识提取与应用方面的复杂性对任务进行分类，从而能够系统评估RAG系统的问题解决能力。这一策略性方法为RAG系统的阶段性开发与增强提供了路线图，旨在满足工业应用不断变化的需求。此外，我们提出了知识原子化与知识感知的任务分解方法，分别用于从数据块中有效提取多方面的知识，以及基于原始查询与累积知识迭代构建推理链，在多个基准测试中均展现出卓越性能。