Retrieval-Augmented Generation (RAG) has become an important paradigm for knowledge-intensive natural language processing, as it enables Large Language Models (LLMs) to access external evidence beyond their parametric memory. However, existing RAG pipelines often rely on static user queries and predominantly semantic matching, which makes them less effective in data-intensive scenarios that require structured knowledge and multi-hop evidence aggregation. To address these limitations, we propose DMAR, a dynamic multi-anchor retrieval framework for retrieval refinement in knowledge-augmented generation. DMAR first identifies high-confidence anchor documents from an initial candidate pool through a dual-path evaluator that combines semantic relevance with knowledge-graph-based structural association. The selected anchors are then used to guide generative query reformulation, producing an enriched query for second-stage retrieval, followed by fidelity-controlled reranking to preserve alignment with the user’s original intent. We further model structural relevance using Subgraph Shapley Values and a learnable Siamese GNN-based similarity module. Experiments on five knowledge-intensive benchmarks, covering open-domain question answering, multi-hop reasoning, and fact verification, show that DMAR consistently improves retrieval and downstream answer quality over strong baselines. For example, DMAR achieves an F1 score of 62.5% on HotpotQA and 79.0% on TriviaQA. These results demonstrate that dynamically integrating semantic retrieval, structural knowledge, and query reformulation is an effective approach for robust knowledge-augmented NLP systems.
Lei et al. (Sun,) studied this question.