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Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement
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Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement
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Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving relevant documents from external sources and incorporating them into the context. While it improves reliability by providing factual texts, it significantly increases inference costs as context length grows and introduces challenging issue of RAG hallucination, primarily caused by the lack of corresponding parametric knowledge in LLMs. An efficient solution is to enhance the knowledge of LLMs at test-time. Parametric RAG (PRAG) addresses this by embedding document into LLMs parameters to perform test-time knowledge enhancement, effectively reducing inference costs through offline training. However, its high training and storage costs, along with limited generalization ability, significantly restrict its practical adoption. To address these challenges, we propose Dynamic Parametric RAG (DyPRAG), a novel framework that leverages a lightweight parameter translator model to efficiently convert documents into parametric knowledge. DyPRAG not only reduces inference, training, and storage costs but also dynamically generates parametric knowledge, seamlessly enhancing the knowledge of LLMs and resolving knowledge conflicts in a plug-and-play manner at test-time. Extensive experiments on multiple datasets demonstrate the effectiveness and generalization capabilities of DyPRAG, offering a powerful and practical RAG paradigm which enables superior knowledge fusion and mitigates RAG hallucination in real-world applications. Our code is available at https://github.com/Trae1ounG/DyPRAG.
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Cited by 18 Pith papers
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MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio
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MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio
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RWGBench: Evaluating Scholarly Positioning in Related Work Generation
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FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapters
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An LLM can memorize a knowledge graph into LoRA weights and answer relation/reasoning queries about it without graph context, but the evaluation partly trains on the test task.
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