mlsanityData to 14 Oct 2026

UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses

arXiv 2506.22210 ↗First posted 27 Jun 2025cs.IRcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Retrieval-augmented generation (RAG) faces challenges related to factual correctness, source attribution, and response completeness. The LiveRAG Challenge hosted at SIGIR'25 aims to advance RAG research using a fixed corpus and a shared, open-source LLM. We propose a modular pipeline that operates on information nuggets-minimal, atomic units of relevant information extracted from retrieved documents. This multistage pipeline encompasses query rewriting, passage retrieval and reranking, nugget detection and clustering, cluster ranking and summarization, and response fluency enhancement. This design inherently promotes grounding in specific facts, facilitates source attribution, and ensures maximum information inclusion within length constraints. In this challenge, we extend our focus to also address the retrieval component of RAG, building upon our prior work on multi-faceted query rewriting. Furthermore, for augmented generation, we concentrate on improving context curation capabilities, maximizing the breadth of information covered in the response while ensuring pipeline efficiency. Our results show that combining original queries with a few sub-query rewrites boosts recall, while increasing the number of documents used for reranking and generation beyond a certain point reduces effectiveness, without improving response quality.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 24 of its 40 references that are in the map. First posted on arXiv 27 Jun 2025.

Builds on

24 of 40 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025GINGER: Grounded Information Nugget-Based Generation of Responses23 Mar
  2. 2025Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana22 Jan
  3. 2024Initial Nugget Evaluation Results for the TREC 2024 RAG Track with the AutoNuggetizer Framework14 Nov
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 20

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  3. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  4. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  5. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  6. 2023LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compressioncited by 453
  7. 2022News Summarization and Evaluation in the Era of GPT-3cited by 327
  8. 2023Evaluating Verifiability in Generative Search Enginescited by 303
  9. 2021Measuring Attribution in Natural Language Generation Modelscited by 256
  10. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 256
  11. 2024Chain of Agents: Large Language Models Collaborating on Long-Context Taskscited by 206
  12. 2021The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Modelscited by 81
8 more
  1. 2023From Sparse to Dense: GPT-4 Summarization with Chain of Density Promptingcited by 52
  2. 2021Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarizationcited by 51
  3. 2023PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adaptercited by 38
  4. 2023When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasetscited by 26
  5. 2024GenQREnsemble: Zero-Shot LLM Ensemble Prompting for Generative Query Reformulationcited by 23
  6. 2024Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writerscited by 19
  7. 2021Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summariescited by 15
  8. 2024A Surprisingly Simple yet Effective Multi-Query Rewriting Method for Conversational Passage Retrievalcited by 14

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025SIGIR 2025 -- LiveRAG Challenge Reportcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems
  2. 2026NuggetIndex: Governed Atomic Retrieval for Maintainable RAG
  3. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  4. 2025Test-time Corpus Feedback: From Retrieval to RAG
  5. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  6. 2024Meta Knowledge for Retrieval Augmented Large Language Models
  7. 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
  8. 2026Beyond the Reranker: Do RAG Retrieval Enhancements Help Once a Strong Reranker Is Present?