mlsanityData to 14 Oct 2026

GINGER: Grounded Information Nugget-Based Generation of Responses

arXiv 2503.18174 ↗First posted 23 Mar 2025cs.CL cs.IRcited by 16

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. To address them, we propose a modular pipeline for grounded response generation that operates on information nuggets-minimal, atomic units of relevant information extracted from retrieved documents. The multistage pipeline encompasses nugget detection, clustering, ranking, top cluster summarization, and fluency enhancement. It guarantees grounding in specific facts, facilitates source attribution, and ensures maximum information inclusion within length constraints. Extensive experiments on the TREC RAG'24 dataset evaluated with the AutoNuggetizer framework demonstrate that GINGER achieves state-of-the-art performance on this benchmark.

Key facts. Cited by 16 papers 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 23 of its 43 references that are in the map. First posted on arXiv 23 Mar 2025.

Builds on

23 of 43 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024The Power of Noise: Redefining Retrieval for RAG Systems26 Jan

Other references · 22

  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. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  4. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  5. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  6. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  7. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  8. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  9. 2023LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compressioncited by 453
  10. 2021Measuring Attribution in Natural Language Generation Modelscited by 256
  11. 2024Chain of Agents: Large Language Models Collaborating on Long-Context Taskscited by 206
  12. 2022Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluationcited by 142
10 more
  1. 2022Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectorscited by 104
  2. 2023From Sparse to Dense: GPT-4 Summarization with Chain of Density Promptingcited by 52
  3. 2021Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarizationcited by 51
  4. 2023PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adaptercited by 38
  5. 2022Conversational Information Seekingcited by 37
  6. 2023SEMQA: Semi-Extractive Multi-Source Question Answeringcited by 19
  7. 2024Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writerscited by 19
  8. 2021Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summariescited by 15
  9. 2023Evaluating Generative Ad Hoc Information Retrievalcited by 11
  10. 2023SWAN: A Generic Framework for Auditing Textual Conversational Systemscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 12.

16 papers.

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  1. 2025Auto-ARGUE: LLM-Based Report Generation Evaluationcited by 14
  2. 2025Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generationcited by 2
  3. 2025An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEscited by 2
  4. 2026Incorporating Q&A Nuggets into Retrieval-Augmented Generationcited by 2
  5. 2026LANCER: LLM Reranking for Nugget Coveragecited by 2
  6. 2025UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responsescited by 1
  7. 2026RoutIR: Fast Serving of Retrieval Pipelines for Retrieval-Augmented Generationcited by 1
  8. 2026Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?cited by 1
  9. 2026Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coveragecited by 1
  10. 2026DR3-Eval: Towards Realistic and Reproducible Deep Research Evaluationcited by 1
  11. 2026NuggetIndex: Governed Atomic Retrieval for Maintainable RAGcited by 1
  12. 2026DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluationcited by 1
4 more
  1. 2026MARQUIS: A Three-Stage Pipeline for Video Retrieval-Augmented Generationcited by 0
  2. 2026Plans for Evaluating Structured Generative Search Summariescited by 0
  3. 2026CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAGcited by 0
  4. 2026MultiVENT-Raw: A Benchmark for Retrieval and Reasoning over Raw Videoscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025InfoGain-RAG: Boosting Retrieval-Augmented Generation via Document Information Gain-based Reranking and Filtering
  2. 2026Uncertainty-Aware Hybrid Retrieval for Long-Document RAG
  3. 2025HF-RAG: Hierarchical Fusion-based RAG with Multiple Sources and Rankers
  4. 2025The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models
  5. 2026A Systems-Level Analysis of Sensitivity, Robustness, and Stability in Retrieval-Augmented Generation
  6. 2025Test-time Corpus Feedback: From Retrieval to RAG
  7. 2025Redefining Retrieval Evaluation in the Era of LLMs
  8. 2025SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression