From Ranking to Selection: A Simple but Efficient Dynamic Passage Selector for Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2508.09497v1
• Date: Wed, 13 Aug 2025 05:05:34 GMT
• Title: From Ranking to Selection: A Simple but Efficient Dynamic Passage Selector for Retrieval Augmented Generation
• Abstract summary: We introduce Dynamic Passage Selector (DPS), a novel reranking framework that treats passage selection as a supervised learning problem.<n>As a seamless plug-and-play module, DPS requires no modifications to the standard RAG pipeline.<n>Our results demonstrate that by enabling adaptive evidence selection, DPS substantially enhances reasoning capabilities in complex RAG scenarios.
• Score: 10.904177110367122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) systems are often bottlenecked by their reranking modules, which typically score passages independently and select a fixed Top-K size. This approach struggles with complex multi-hop queries that require synthesizing evidence across multiple documents, creating a trade-off where small K values omit crucial information and large K values introduce noise. To address this, we introduce the Dynamic Passage Selector (DPS), a novel reranking framework that treats passage selection as a supervised learning problem. Unlike traditional point-wise or list-wise methods, DPS is fine-tuned to capture inter-passage dependencies and dynamically select the most relevant set of passages for generation. As a seamless plug-and-play module, DPS requires no modifications to the standard RAG pipeline. Comprehensive evaluations on five benchmarks show that DPS consistently outperforms state-of-the-art rerankers and fine-tuning methods. Notably, on the challenging MuSiQue dataset, DPS improves the F1-score by 30.06% and 15.4% over strong baselines like Qwen3-reranker and RankingGPT, respectively. Our results demonstrate that by enabling adaptive evidence selection, DPS substantially enhances reasoning capabilities in complex RAG scenarios.

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