Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement (2503.23895v4)

Abstract: Retrieval-augmented generation (RAG) enhances 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.

• The paper introduces Dynamic Parametric Retrieval Augmented Generation (DyPRAG), a novel framework that embeds external knowledge into LLM parameters dynamically at test-time via a lightweight parameter translator.
• DyPRAG employs this parameter translator to convert documents into parametric knowledge during inference, demonstrating superior generalization and effectiveness compared to existing RAG methods in empirical evaluations.
• Practically, DyPRAG offers a cost-effective framework for RAG systems to efficiently internalize unseen knowledge, enabling more responsive AI in domains requiring frequent updates.

Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement

The paper "Better wit than wealth: Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement" presents a novel approach to improving the performance and efficiency of Retrieval-augmented Generation (RAG) systems through Dynamic Parametric Retrieval-augmented Generation (DyPRAG). Traditional RAG systems enhance LLMs by incorporating external documents to enrich contextual information. However, this often increases inference costs and can introduce hallucinations due to inaccuracies or incoherence between internal and external knowledge. The paper proposes a method to address these limitations via a dynamic, parametric approach.

Methodological Innovations

The key innovation presented in this work is the DyPRAG framework. This approach departs from the standard RAG by embedding external knowledge directly into the parameters of LLMs during inference, circumventing the need for lengthy contexts. This is achieved through a lightweight parameter translator model that dynamically converts documents into parametric knowledge in a plug-and-play manner at test-time.

1. Dynamic Conversion: DyPRAG employs a parameter translator, a small hypernetwork model to transform document embeddings into parameters for LLMs. This reduces the need for retraining or storing extensive parametric representations, crucially lowering the storage and computation requirements.
2. Test-time Knowledge Enhancement: By enabling dynamic parameter injection, DyPRAG allows LLMs to access updated or new external knowledge efficiently without high costs associated with offline parameter training.
3. RAG Hallucination Mitigation: Through seamless integration of parametric knowledge, DyPRAG aims to resolve knowledge conflicts that arise during retrieval-augmented generation by ensuring coherence between internal and external information.

Empirical Evaluation

The paper conducts extensive experiments on multiple datasets to validate the effectiveness of DyPRAG. The results demonstrate superior generalization ability and effectiveness, achieving better performance across different model scales when compared to both standard RAG and previous Parametric RAG (PRAG) methods. Notably, DyPRAG combines contextual and parametric knowledge effectively, a method referred to as DyPRAG-Combine, which shows improved knowledge fusion and reduced hallucination rates.

Theoretical and Practical Implications

Theoretically, this work contributes to the understanding of how parametric knowledge can be efficiently integrated into LLMs without excessive computational burdens, presenting a scalable solution for real-world applications requiring frequent knowledge updates.

Practically, DyPRAG offers a robust framework for developing RAG systems that are both cost-effective and capable of internalizing unseen knowledge efficiently. This capability enables the deployment of more responsive AI systems in domains where knowledge rapidly evolves or requires frequent updates, such as healthcare or legal advisories.

Speculation on Future Developments

Future research could explore expanding DyPRAG's capabilities to other domains beyond question-answering, such as conversational AI or real-time decision support systems. The framework's potential for seamless knowledge integration presents opportunities for creating more adaptive and intelligent systems that can operate under dynamic information environments.

Conclusion

The DyPRAG framework stands as a significant step forward in the evolution of retrieval-augmented generation methods. By effectively balancing inference efficiency with enhanced knowledge integration, it charts a path towards more sophisticated and reliable AI systems capable of navigating complex information landscapes. As the field progresses, this approach may serve as a foundational model for next-generation RAG systems, bridging gaps between static knowledge retrieval and dynamic, real-time information synthesis.