RAG+：通过应用感知推理增强检索增强生成
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
AI总结：
RAG+通过联合检索知识与应用示例，将应用感知推理显式融入RAG流程，在数学、法律和医学任务中平均提升3-5%，最高提升13.5%。
摘要：
通过检索增强生成（RAG）整合外部知识，已成为增强大语言模型（LLMs）处理知识密集型任务的基础方法。然而，现有的RAG范式往往忽略了应用知识的认知步骤，导致检索到的事实与任务特定推理之间存在差距。在本工作中，我们提出RAG+，这是一个原则性的模块化扩展，明确将应用感知推理纳入RAG流程。RAG+构建一个由知识和对齐的应用示例组成的双重语料库，该语料库可以手动或自动创建，并在推理过程中联合检索两者。这种设计使LLMs不仅能够访问相关信息，还能在结构化的、目标导向的推理过程中应用这些信息。在数学、法律和医学领域，基于多个模型进行的实验表明，RAG+始终优于标准RAG变体，平均提升3-5%，在复杂场景中最高提升达13.5%。通过将检索与可操作的应用相连接，RAG+推进了一个更具认知基础的知识整合框架，代表着向更具可解释性和更强能力的LLMs迈出的一步。
英文摘要：
The integration of external knowledge through Retrieval-Augmented Generation (RAG) has become foundational in enhancing large language models (LLMs) for knowledge-intensive tasks. However, existing RAG paradigms often overlook the cognitive step of applying knowledge, leaving a gap between retrieved facts and task-specific reasoning. In this work, we introduce RAG+, a principled and modular extension that explicitly incorporates application-aware reasoning into the RAG pipeline. RAG+ constructs a dual corpus consisting of knowledge and aligned application examples, created either manually or automatically, and retrieves both jointly during inference. This design enables LLMs not only to access relevant information but also to apply it within structured, goal-oriented reasoning processes. Experiments across mathematical, legal, and medical domains, conducted on multiple models, demonstrate that RAG+ consistently outperforms standard RAG variants, achieving average improvements of 3-5%, and peak gains up to 13.5% in complex scenarios. By bridging retrieval with actionable application, RAG+ advances a more cognitively grounded framework for knowledge integration, representing a step toward more interpretable and capable LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
