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SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval
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SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval
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Retrieval-Augmented Generation (RAG) has greatly improved large language models (LLMs) by enabling them to generate accurate, contextually grounded responses through the integration of external information. However, conventional RAG approaches, which prioritize top-ranked documents based solely on query-context relevance, often introduce redundancy and conflicting information. This issue is particularly evident in unsupervised retrieval settings, where there are no mechanisms to effectively mitigate these problems, leading to suboptimal context selection. To address this, we propose Selection using Matrices for Augmented Retrieval (SMART) in question answering tasks, a fully unsupervised and training-free framework designed to optimize context selection in RAG. SMART leverages Determinantal Point Processes (DPPs) to simultaneously model relevance, diversity and conflict, ensuring the selection of potentially high-quality contexts. Experimental results across multiple datasets demonstrate that SMART significantly enhances QA performance and surpasses previous unsupervised context selection methods, showing a promising strategy for RAG.
Forward citations
Cited by 4 Pith papers
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Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation
GeoRAG recasts RAG context selection as monotone submodular Information Demand Coverage Optimization solved via Sinkhorn-Wasserstein distance, delivering +6.5 to +7.5 EM gains over top-k on six QA benchmarks.
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FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A
FLOWREADER reframes multimodal long-document evidence assembly as min-cost flow optimization and reports competitive results on VisDoMBench subsets with fragmented evidence.
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Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval
Factually correct but one-sided passages injected into a RAG database can shift LLM answers toward an attacker's stance, and a PCA/KL-based filter (BiasDef) reduces that shift.
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A Reproducibility Study of Metacognitive Retrieval-Augmented Generation
MetaRAG is only partially reproducible with lower absolute scores than originally reported, gains substantially from reranking, and shows greater robustness than SIM-RAG under extended retrieval features.
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