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Latent RAG is introduced, a novel paradigm that performs knowledge injection entirely within the continuous latent space, and enables more natural knowledge integration while achieving 9,200X storage reduction compared to Parametric RAG.
This work proposes Consistency-Calibrated Uncertainty Fusion (CCUF), a framework that calibrates individual model uncertainties using cross-model consistency scores and enables more reliable answer selection for factoid question answering.
This work hypothesizes that the teacher model's capacity acts as an information bottleneck, limiting how much large student models can learn, and provides practical guidance for selecting appropriate teacher models when training large-scale neural retrievers with knowledge distillation.
SCORE-RAG reformulates multi-hop RAG as a two-phase adaptive process: exploration for dynamic query understanding, followed by exploitation for precise evidence gathering, which enables adaptive query comprehension, reduces error accumulation via self-verification, and produces interpretable reasoning chains for accura...
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