构建领域数据生成框架，提升RAG系统在专业场景下的表现
Domain-Specific Data Generation Framework for RAG Adaptation
- 基于概念提取与布卢姆分类法生成多样化问题
- 支持多种RAG适配策略，提升模型推理能力
- 模块化设计适合科学与企业知识库等动态领域
检索增强生成（RAG）通过结合大语言模型（LLM）的语言理解与外部检索能力，实现领域相关响应。有效适配RAG系统至特定领域需依赖专业且上下文丰富的训练数据，而不仅限于通用问答数据。本文提出RAGen——一个可扩展、模块化的领域数据生成框架，用于生成针对不同RAG适配方法的领域相关问答-上下文（QAC）三元组。RAGen通过识别文档中的关键概念，依据布卢姆分类法原则生成多样化问题，并将问题与来自相关上下文的精准答案配对。该框架支持多种RAG适配策略，包括优化大语言模型、检索器和嵌入模型等核心组件。其模块化流程包含语义分块、层级概念提取与多块检索，并引入精选干扰上下文以促进鲁棒推理。该框架具备良好的可扩展性，能高效处理大规模且持续演进的文档集合，无需重复计算，特别适用于科研进展迅速或企业知识库持续更新的动态领域。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models (LLMs) with external retrieval to enable domain-grounded responses. Effectively adapting RAG systems to domain-specific settings requires specialized, context-rich training data beyond general-purpose question-answering. Here, we propose RAGen, a scalable and modular framework for generating domain-grounded question-answer-context (QAC) triples tailored to diverse RAG adaptation approaches. RAGen produces these QAC triples by identifying key concepts in documents, generating diverse questions guided by Bloom's Taxonomy-inspired principles, and pairing them with precise answers extracted from relevant contexts. RAGen supports multiple RAG adaptation strategies, including the optimization of key components such as the LLM, retriever, and embedding model, etc. Its modular pipeline features semantic chunking, hierarchical concept extraction, and multi-chunk retrieval, along with the introduction of curated distractor contexts to promote robust reasoning. Designed for scalability, RAGen efficiently handles large and evolving document corpora without redundant processing, making it especially suitable for dynamic evolving domains such as scientific research and enterprise knowledge bases.
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