DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation
arXiv:2603.18012v1 Announce Type: new Abstract: We present DynaRAG, a retrieval-augmented generation (RAG) framework designed to handle both static and time-sensitive information needs through dynamic knowledge integration. Unlike traditional RAG pipelines that rely solely on static corpora, DynaRAG selectively invokes external APIs when retrieved documents are insufficient for answering a query. The system employs an LLM-based reranker to assess document relevance, a sufficiency classifier to determine when fallback is necessary, and Gorilla v2 -- a state-of-the-art API calling model -- for accurate tool invocation. We further enhance robustness by incorporating schema filtering via FAISS to guide API selection. Evaluations on the CRAG benchmark demonstrate that DynaRAG significantly improves accuracy on dynamic questions, while also reducing hallucinations. Our results highlight the importance of dynamic-aware routing and selective tool use in building reliable, real-world question-
arXiv:2603.18012v1 Announce Type: new Abstract: We present DynaRAG, a retrieval-augmented generation (RAG) framework designed to handle both static and time-sensitive information needs through dynamic knowledge integration. Unlike traditional RAG pipelines that rely solely on static corpora, DynaRAG selectively invokes external APIs when retrieved documents are insufficient for answering a query. The system employs an LLM-based reranker to assess document relevance, a sufficiency classifier to determine when fallback is necessary, and Gorilla v2 -- a state-of-the-art API calling model -- for accurate tool invocation. We further enhance robustness by incorporating schema filtering via FAISS to guide API selection. Evaluations on the CRAG benchmark demonstrate that DynaRAG significantly improves accuracy on dynamic questions, while also reducing hallucinations. Our results highlight the importance of dynamic-aware routing and selective tool use in building reliable, real-world question-answering systems.
Executive Summary
DynaRAG, a novel retrieval-augmented generation (RAG) framework, is presented in this study. DynaRAG integrates static and dynamic knowledge to address time-sensitive information needs. By selectively invoking external APIs and leveraging LLM-based reranking, sufficiency classification, and schema filtering, DynaRAG enhances the accuracy and reliability of question-answering systems. Evaluations on the CRAG benchmark demonstrate significant improvements in dynamic question accuracy and reduced hallucinations. This work highlights the importance of dynamic-aware routing and selective tool use in building robust real-world question-answering systems. The study's findings have significant implications for the development of reliable and accurate AI-powered question-answering systems, particularly in applications where timely and relevant information is crucial.
Key Points
- ▸ DynaRAG integrates static and dynamic knowledge to address time-sensitive information needs.
- ▸ Selective API invocation enhances the accuracy and reliability of question-answering systems.
- ▸ LLM-based reranking, sufficiency classification, and schema filtering are key components of DynaRAG.
Merits
Strength in Addressing Dynamic Information Needs
DynaRAG's ability to selectively invoke external APIs and integrate dynamic knowledge addresses a significant limitation of traditional RAG pipelines, which rely solely on static corpora.
Improved Accuracy and Reliability
DynaRAG's use of LLM-based reranking, sufficiency classification, and schema filtering enhances the accuracy and reliability of question-answering systems, particularly in dynamic question-answering scenarios.
Demerits
Limitation in API Invocation
The study's reliance on external APIs as a fallback mechanism may introduce additional latency and variability in system performance, which could be mitigated by exploring alternative approaches to dynamic knowledge integration.
Limited Evaluation on Static Questions
The study's focus on dynamic question-answering scenarios may limit the generalizability of DynaRAG's findings to static question-answering applications, where traditional RAG pipelines may still be sufficient.
Expert Commentary
DynaRAG represents a significant advancement in the development of question-answering systems that can effectively handle dynamic information needs. By selectively invoking external APIs and leveraging LLM-based reranking, sufficiency classification, and schema filtering, DynaRAG demonstrates a more robust and reliable approach to dynamic question-answering. The study's findings have important implications for the development of AI-powered question-answering systems, particularly in applications where timely and relevant information is crucial. However, the study's reliance on external APIs as a fallback mechanism and limited evaluation on static questions remain areas for further research and exploration.
Recommendations
- ✓ Future research should investigate alternative approaches to dynamic knowledge integration, such as the use of multimodal inputs or graph-based knowledge representation.
- ✓ The study's findings should be further evaluated on a wider range of question-answering scenarios, including those involving static questions and multiple modalities.
