mlsanityData to 14 Oct 2026

Shifting from Ranking to Set Selection for Retrieval Augmented Generation

arXiv 2507.06838 ↗First posted 9 Jul 2025cs.CL cs.IRcited by 15

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

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

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 33 of its 42 references that are in the map. First posted on arXiv 9 Jul 2025.

Builds on

33 of 42 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval31 Jan
  2. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy24 May

Other references · 31

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  5. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  6. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  7. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  8. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  9. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  10. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  11. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  12. 2023In-Context Retrieval-Augmented Language Modelscited by 702
19 more
  1. 2019Passage Re-ranking with BERTcited by 645
  2. 2023Zephyr: Direct Distillation of LM Alignmentcited by 506
  3. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  4. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 307
  5. 2020Overview of the TREC 2019 deep learning trackcited by 286
  6. 2023Fine-Tuning LLaMA for Multi-Stage Text Retrievalcited by 272
  7. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 257
  8. 2021Overview of the TREC 2020 deep learning trackcited by 196
  9. 2024Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaborationcited by 180
  10. 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 155
  11. 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 115
  12. 2024ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidencecited by 113
  13. 2023Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Studycited by 63
  14. 2023Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noisecited by 54
  15. 2024JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Rerankingcited by 34
  16. 2024ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrievalcited by 30
  17. 2024From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queriescited by 16
  18. 2025Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generationcited by 13
  19. 2024An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Rerankingcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 12.

15 papers.

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  1. 2025PRISM: Agentic Retrieval with LLMs for Multi-Hop Question Answeringcited by 8
  2. 2026SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answeringcited by 6
  3. 2026Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Rankingcited by 3
  4. 2026FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapterscited by 2
  5. 2026State-Centric Decision Processcited by 1
  6. 2026AdaTutoRank: Learning to Rerank Document Sets via Adaptive Tutoring Optimization for RAG and Deep Researchcited by 1
  7. 2025Experience Scaling: Post-Deployment Evolution For Large Language Modelscited by 0
  8. 2025Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Groundingcited by 0
  9. 2026Why Retrieval-Augmented Generation Fails: A Graph Perspectivecited by 0
  10. 2026Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerabilitycited by 0
  11. 2026Retrieving a Set, Not Independent Passages: Set-Level Compatibility Learning for Efficient Set Explorationcited by 0
  12. 2026EviReform: Evidence-Guided Query Reformulation for Multi-Hop Graph Retrievalcited by 0
3 more
  1. 2026Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrievalcited by 0
  2. 2026SEEDS: Preview-Guided Evidence Admission for Search Agentscited by 0
  3. 2026Recall What Is Missing: Composing Evidence from Long-Term Agent Memorycited by 0

Similar, not cited

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  1. 2026Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering
  2. 2024EfficientRAG: Efficient Retriever for Multi-Hop Question Answering
  3. 2025Investigating Retrieval-Augmented Generation Systems on Unanswerable, Uncheatable, Realistic, Multi-hop Queries
  4. 2026ConRAG: Consensus-Driven Multi-View Retrieval for Multi-Hop Question Answering
  5. 2025Optimizing Multi-Hop Document Retrieval Through Intermediate Representations
  6. 2024Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
  7. 2026OptiSet: Unified Optimizing Set Selection and Ranking for Retrieval-Augmented Generation
  8. 2025Question Decomposition for Retrieval-Augmented Generation