Fine-grained entity recognition is crucial for reasoning and decision-making in task-oriented dialogues, yet current large language models (LLMs) continue to face challenges in domain adaptation and retrieval controllability. We introduce MME-RAG, a Multi-Manager-Expert Retrieval-Augmented Generation framework that decomposes entity recognition into two coordinated stages: type-level judgment by lightweight managers and span-level extraction by specialized experts. Each expert is supported by a KeyInfo retriever that injects semantically aligned, few-shot exemplars during inference, enabling precise and domain-adaptive extraction without additional training. Experiments on CrossNER, MIT-Movie, MIT-Restaurant, and our newly constructed multi-domain customer-service dataset demonstrate that MME-RAG performs better than recent baselines in most domains. Ablation studies further show that both the hierarchical decomposition and KeyInfo-guided retrieval are key drivers of robustness and cross-domain generalization, establishing MME-RAG as a scalable and interpretable solution for adaptive dialogue understanding.

翻译：细粒度实体识别对于任务型对话中的推理与决策至关重要，然而当前的大语言模型（LLMs）在领域适应性与检索可控性方面仍面临挑战。本文提出MME-RAG，一种多管理器-专家检索增强生成框架，将实体识别分解为两个协同阶段：由轻量级管理器执行类型级判断，并由专业化专家执行跨度级抽取。每个专家均得到KeyInfo检索器的支持，该检索器在推理过程中注入语义对齐的少样本示例，从而无需额外训练即可实现精确且领域自适应的抽取。在CrossNER、MIT-Movie、MIT-Restaurant以及我们新构建的多领域客服数据集上的实验表明，MME-RAG在多数领域中的表现优于近期基线方法。消融研究进一步证明，层次化分解与KeyInfo引导的检索均是提升模型鲁棒性与跨领域泛化能力的关键因素，确立了MME-RAG作为一种可扩展且可解释的自适应对话理解解决方案。