Parametric Retrieval-Augmented Generation using Latent Routing of LoRA Adapters

• URL: http://arxiv.org/abs/2511.17044v1
• Date: Fri, 21 Nov 2025 08:44:21 GMT
• Title: Parametric Retrieval-Augmented Generation using Latent Routing of LoRA Adapters
• Abstract summary: Parametric Retrieval-Augmented Generation (PRAG) integrates external knowledge directly into a Large Language Model (LLM)<n>Current PRAG approaches adopt a textbfone-to-one document encoding scheme, using a dedicated LoRA adapter for each individual document.<n>We propose a novel paradigm for encoding passages in PRAG that utilizes a latent routing encoding process.
• Score: 27.694134466842502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parametric Retrieval-Augmented Generation (PRAG) is a novel RAG paradigm that integrates external knowledge directly into a Large Language Model (LLM) by parameterizing documents using LoRA adapters, demonstrating reduced inference costs compared to traditional RAG approaches. However, current PRAG approaches adopt a \textbf{one-to-one} document encoding scheme, using a dedicated LoRA adapter for each individual document. This scheme introduces two major limitations: First, it leads to data scarcity, as the training datasets for individual LoRA adapters are limited. Second, it incurs high overhead during inference, requiring the merging of LLM weights with a new LoRA adapter for every candidate passage, which is computationally inefficient. To overcome these challenges, we propose a novel paradigm for encoding passages in PRAG that utilizes a latent routing encoding process (Poly-PRAG). During offline encoding, we treat the encoding of a set of documents as a multi-task learning process, where each passage is assigned a unique task identifier. By employing a routing function, we use a small set of latent LoRA adapters to encode the entire passage space. During online inference, this routing function selectively activates a subset of latent experts based on the input query. We conduct comprehensive evaluations of Poly-PRAG across multiple knowledge-intensive NLP tasks. Our extensive experiments demonstrate the effectiveness of the proposed method, achieving state-of-the-art results on four distinct datasets.

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