OptiSet: Unified Optimizing Set Selection and Ranking for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2601.05027v1
• Date: Thu, 08 Jan 2026 15:35:01 GMT
• Title: OptiSet: Unified Optimizing Set Selection and Ranking for Retrieval-Augmented Generation
• Authors: Yi Jiang, Sendong Zhao, Jianbo Li, Bairui Hu, Yanrui Du, Haochun Wang, Bing Qin,
• Abstract summary: Retrieval-Augmented Generation (RAG) improves generation quality by incorporating evidence retrieved from large external corpora.<n>We propose OptiSet, a set-centric framework that unifies set selection and set-level ranking for RAG.
• Score: 46.01696202049653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) improves generation quality by incorporating evidence retrieved from large external corpora. However, most existing methods rely on statically selecting top-k passages based on individual relevance, which fails to exploit combinatorial gains among passages and often introduces substantial redundancy. To address this limitation, we propose OptiSet, a set-centric framework that unifies set selection and set-level ranking for RAG. OptiSet adopts an "Expand-then-Refine" paradigm: it first expands a query into multiple perspectives to enable a diverse candidate pool and then refines the candidate pool via re-selection to form a compact evidence set. We then devise a self-synthesis strategy without strong LLM supervision to derive preference labels from the set conditional utility changes of the generator, thereby identifying complementary and redundant evidence. Finally, we introduce a set-list wise training strategy that jointly optimizes set selection and set-level ranking, enabling the model to favor compact, high-gain evidence sets. Extensive experiments demonstrate that OptiSet improves performance on complex combinatorial problems and makes generation more efficient. The source code is publicly available.

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