ExpertRAG：基于混合专家模型的高效RAG——优化上下文检索以实现自适应LLM响应
ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses
AI总结：
本文提出ExpertRAG框架，将混合专家架构与检索增强生成结合，通过动态检索门控机制实现查询驱动的知识检索与专家路由，在理论上证明其计算与知识利用效率优势，并提出实验验证策略。
摘要：
ExpertRAG是一种新颖的理论框架，它将混合专家（MoE）架构与检索增强生成（RAG）相结合，以提升知识密集型语言建模的效率和准确性。我们提出了一种动态检索门控机制，结合专家路由，使模型能够根据查询需求选择性地查阅外部知识库或依赖专门的内部专家。本文阐述了ExpertRAG的理论基础，包括将检索和专家选择视为潜在决策的概率公式，以及其在计算和知识利用效率方面的数学证明。我们推导了量化选择性检索带来的预期计算成本节约和稀疏专家利用带来的能力提升的公式。通过对比分析，将ExpertRAG与标准RAG（始终开启检索）和纯MoE模型（如Switch Transformer、Mixtral）进行定位，以突出其在参数化知识和非参数化检索之间的独特平衡。我们还概述了实验验证策略，提出了基准和评估协议，以测试ExpertRAG在事实召回、泛化和推理效率方面的性能。所提出的框架虽以理论形式呈现，但得到了RAG和MoE先前工作的见解支持，有望通过利用两种范式的优势提供更具事实性、高效且自适应的生成。总之，ExpertRAG为语言模型的扩展和增强提供了新视角，并得到了全面分析和实证验证路线图的支持。
英文摘要：
ExpertRAG is a novel theoretical framework that integrates Mixture-of-Experts (MoE) architectures with Retrieval Augmented Generation (RAG) to advance the efficiency and accuracy of knowledge-intensive language modeling. We propose a dynamic retrieval gating mechanism coupled with expert routing, enabling the model to selectively consult an external knowledge store or rely on specialized internal experts based on the query's needs. The paper lays out the theoretical foundations of ExpertRAG, including a probabilistic formulation that treats retrieval and expert selection as latent decisions, and mathematical justifications for its efficiency in both computation and knowledge utilization. We derive formulae to quantify the expected computational cost savings from selective retrieval and the capacity gains from sparse expert utilization. A comparative analysis positions ExpertRAG against standard RAG (with always-on retrieval) and pure MoE models (e.g., Switch Transformer, Mixtral) to highlight its unique balance between parametric knowledge and non-parametric retrieval. We also outline an experimental validation strategy, proposing benchmarks and evaluation protocols to test ExpertRAG's performance on factual recall, generalization, and inference efficiency. The proposed framework, although presented theoretically, is supported by insights from prior work in RAG and MoE, and is poised to provide more factual, efficient, and adaptive generation by leveraging the best of both paradigms. In summary, ExpertRAG contributes a new perspective on scaling and augmenting language models, backed by a thorough analysis and a roadmap for empirical validation.
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。
