DOME adapts generative IR models to unseen documents via critical-layer identification, hybrid-label edit vector optimization, and parameter updates, achieving strong new-document retrieval with reduced training cost.
Generative retrieval with large language models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
Active Indexing with synthetic data augmentation for bidirectional fact-source binding during pretraining yields up to 30.2% higher citation precision than passive identifier appending on CitePretrainBench for Qwen models.
citing papers explorer
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Model Editing for New Document Integration in Generative Information Retrieval
DOME adapts generative IR models to unseen documents via critical-layer identification, hybrid-label edit vector optimization, and parameter updates, achieving strong new-document retrieval with reduced training cost.
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Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models
Active Indexing with synthetic data augmentation for bidirectional fact-source binding during pretraining yields up to 30.2% higher citation precision than passive identifier appending on CitePretrainBench for Qwen models.