Shifting from Ranking to Set Selection for Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2507.06838v2
• Date: Thu, 10 Jul 2025 01:36:33 GMT
• Title: Shifting from Ranking to Set Selection for Retrieval Augmented Generation
• Authors: Dahyun Lee, Yongrae Jo, Haeju Park, Moontae Lee,
• Abstract summary: Retrieval in Retrieval-Augmented Generation must ensure that retrieved passages are not only individually relevant but also collectively form a comprehensive set.<n>We propose a set-wise passage selection approach and introduce SETR, which explicitly identifies the information requirements of a query through Chain-of-Thought reasoning.<n>Experiments on multi-hop RAG benchmarks show that SETR outperforms both proprietary LLM-based rerankers and open-source baselines in terms of answer correctness and retrieval quality.
• Score: 16.374737228461125
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retrieval in Retrieval-Augmented Generation(RAG) must ensure that retrieved passages are not only individually relevant but also collectively form a comprehensive set. Existing approaches primarily rerank top-k passages based on their individual relevance, often failing to meet the information needs of complex queries in multi-hop question answering. In this work, we propose a set-wise passage selection approach and introduce SETR, which explicitly identifies the information requirements of a query through Chain-of-Thought reasoning and selects an optimal set of passages that collectively satisfy those requirements. Experiments on multi-hop RAG benchmarks show that SETR outperforms both proprietary LLM-based rerankers and open-source baselines in terms of answer correctness and retrieval quality, providing an effective and efficient alternative to traditional rerankers in RAG systems. The code is available at https://github.com/LGAI-Research/SetR

Related papers

• Generalized Reinforcement Learning for Retriever-Specific Query Rewriter with Unstructured Real-World Documents [4.200973008100858]
  textbfRL-QR is a reinforcement learning framework for retriever-specific query rewriting.<n> RL-QR trains query rewriters tailored to specific retrievers, enhancing retrieval performance across varied domains.<n>Our findings highlight RL-QR's potential to revolutionize query optimization for RAG systems.
  arXiv  Detail & Related papers  (2025-07-31T04:55:21Z)
• Distilling a Small Utility-Based Passage Selector to Enhance Retrieval-Augmented Generation [77.07879255360342]
  Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating retrieved information.<n>In RAG, the emphasis has shifted to utility, which considers the usefulness of passages for generating accurate answers.<n>Our approach focuses on utility-based selection rather than ranking, enabling dynamic passage selection tailored to specific queries without the need for fixed thresholds.<n>Our experiments demonstrate that utility-based selection provides a flexible and cost-effective solution for RAG, significantly reducing computational costs while improving answer quality.
  arXiv  Detail & Related papers  (2025-07-25T09:32:29Z)
• Question Decomposition for Retrieval-Augmented Generation [2.6409776648054764]
  We propose a RAG pipeline that incorporates question decomposition into sub-questions.<n>We show that question decomposition effectively assembles complementary documents, while reranking reduces noise.<n>Although reranking itself is standard, we show that pairing an off-the-shelf cross-encoder reranker with LLM-driven question decomposition bridges the retrieval gap on multi-hop questions.
  arXiv  Detail & Related papers  (2025-07-01T01:01:54Z)
• LTRR: Learning To Rank Retrievers for LLMs [53.285436927963865]
  We show that routing-based RAG systems can outperform the best single-retriever-based systems.<n>Performance gains are especially pronounced in models trained with the Answer Correctness (AC) metric.<n>As part of the SIGIR 2025 LiveRAG challenge, our submitted system demonstrated the practical viability of our approach.
  arXiv  Detail & Related papers  (2025-06-16T17:53:18Z)
• MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation [34.66546005629471]
  Large Language Models (LLMs) are essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information.<n>Retrieval-Augmented Generation (RAG) addresses this issue by incorporating external, real-time information retrieval to ground LLM responses.<n>To tackle this problem, we propose Multi-Agent Filtering Retrieval-Augmented Generation (MAIN-RAG)<n>MAIN-RAG is a training-free RAG framework that leverages multiple LLM agents to collaboratively filter and score retrieved documents.
  arXiv  Detail & Related papers  (2024-12-31T08:07:26Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively.<n>We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers [66.55612528039894]
  AdaQR is a framework for training query rewriting models with limited rewrite annotations from seed datasets and completely no passage label. A novel approach is proposed to assess retriever's preference for these candidates by the probability of answers conditioned on the conversational query.
  arXiv  Detail & Related papers  (2024-06-16T16:09:05Z)
• RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation [42.82192656794179]
  Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pretraining datasets, making them susceptible to errors in unseen scenarios. Retrieval-Augmented Generation (RAG) addresses this by incorporating external, relevant documents into the response generation process.
  arXiv  Detail & Related papers  (2024-03-31T08:58:54Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• Joint Passage Ranking for Diverse Multi-Answer Retrieval [56.43443577137929]
  We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a question. This task requires joint modeling of retrieved passages, as models should not repeatedly retrieve passages containing the same answer at the cost of missing a different valid answer. In this paper, we introduce JPR, a joint passage retrieval model focusing on reranking. To model the joint probability of the retrieved passages, JPR makes use of an autoregressive reranker that selects a sequence of passages, equipped with novel training and decoding algorithms.
  arXiv  Detail & Related papers  (2021-04-17T04:48:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.