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INKER: Adaptive dynamic retrieval augmented generation with internal-external knowledge integration
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DOI：10.1016/j.ipm.2025.104534.png)
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Abstract
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Adaptive dynamic retrieval-augmented generation (RAG) paradigm dynamically determines whether the large language model (LLM) needs to activate the retrieval step during the generation process, and accordingly formulates appropriate queries for retrieval. This paradigm has two key components: determining the optimal moment to activate the retrieval module (when to retrieve) and formulating the appropriate query after retrieval is triggered (what to retrieve). However, existing adaptive dynamic RAG methods rely on the internal knowledge of the LLM to trigger the retrieval process and formulate retrieval queries, largely neglecting the significance of the external query knowledge. This leads to unreliable retrieval timing and the inability to retrieve truly relevant documents. To overcome these limitations, we introduce a new adaptive dynamic RAG framework, Internal-External Knowledge Integration based Retrieval (INKER), which integrates both internal and external knowledge involved in the LLM text generation process to decide when and what to retrieve. Experiments on 2WikiMultihopQA, HotpotQA, StrategyQA, and Natural Questions (NQ) demonstrate that INKER outperforms six advanced RAG methods in terms of accuracy, while also reducing retrieval frequency by approximately 40 % on average, verifying the effectiveness of INKER and its components.
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