PIKE-RAG通过构建推理链增强工业级知识问答能力
PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation
- 将知识拆解为原子单元并分步构建推理链
- 在多个基准上表现优于传统RAG系统
- 适合需要深度专业推理的工业应用场景
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的能力，但在复杂多样的工业应用中仍难以满足需求。单纯依赖检索无法有效提取特定领域知识并完成逻辑推理。为此，我们提出PIKE-RAG，聚焦于专业化知识的提取、理解与应用，并构建连贯的推理链条，逐步引导大模型生成准确答案。针对工业任务的多样性挑战，我们提出基于知识提取与应用复杂度的任务分类新范式，系统评估RAG系统的求解能力。该策略为RAG系统的分阶段演进提供路线图。此外，我们引入知识原子化和知识感知的任务分解方法，从数据块中高效提取多维度知识，并基于原始查询与累积知识迭代构建推理过程，在多个基准测试中展现出卓越性能。
原文摘要 · Abstract (English)
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.
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