Abstract
Federated Retrieval (FR) routes queries across multiple external knowledge sources, to mitigate hallucinations of LLMs, when necessary external knowledge is distributed. However, existing methods struggle to retrieve high-quality and relevant documents for ambiguous queries, especially in cross-domain scenarios, which significantly limits their effectiveness in supporting downstream generation tasks. Inspired by dynamic information flow (DIF), we propose DFAMS, a novel framework that leverages DIF to identify latent query intents and construct semantically aligned knowledge partitions for accurate retrieval across heterogeneous sources. Specifically, DFAMS probes the DIF in LLMs by leveraging gradient signals from a few annotated queries and employing Shapley value-based attribution to trace neuron activation paths associated with intent recognition and subdomain boundary detection. Then, DFAMS leverages DIF to train an alignment module via multi-prototype contrastive learning, enabling fine-grained intra-source modeling and inter-source semantic alignment across knowledge bases. Experimental results across five benchmarks show that DFAMS outperforms advanced FR methods by up to 14.37% in knowledge classification accuracy, 5.38% in retrieval recall, and 6.45% in downstream QA accuracy, demonstrating its effectiveness in complex FR scenarios.
Abstract (translated)
联邦检索（FR）在必要时将查询分布在多个外部知识源上，以减轻大规模语言模型的幻觉问题，尤其是在所需的知识分散的情况下。然而，现有的方法难以针对含糊不清的查询，特别是在跨领域场景中，提取高质量且相关的文档，这大大限制了它们支持下游生成任务的有效性。 受动态信息流（DIF）启发，我们提出了DFAMS这一新型框架，该框架利用DIF来识别潜在的查询意图并构建语义对齐的知识分区，从而在异构来源之间实现准确检索。具体而言，DFAMS通过使用少量注释查询的梯度信号以及基于Shapley值归因的方法探查LLM中的动态信息流，以追踪与意图识别和子域边界检测相关的神经元激活路径。然后，DFAMS利用DIF训练一个对齐模块，通过多原型对比学习实现细粒度的源内建模，并在知识库之间进行语义对齐。 跨五个基准测试的数据表明，在知识分类准确率、检索召回率以及下游问答准确率方面，DFAMS分别比最先进的FR方法高出14.37%、5.38%和6.45%，展示了其在复杂联邦检索场景中的有效性。
URL
https://arxiv.org/abs/2508.20353
