# GINGER: Grounded Information Nugget-Based Generation of Responses

arXiv: https://arxiv.org/abs/2503.18174 · HTML: https://mlsanity.com/paper/2503.18174/

## 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.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 23 of 23)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://mlsanity.com/paper/2310.06839/) (2023)
- [The Power of Noise: Redefining Retrieval for RAG Systems](https://mlsanity.com/paper/2401.14887/) (2024)
- [Measuring Attribution in Natural Language Generation Models](https://mlsanity.com/paper/2112.12870/) (2021)
- [Chain of Agents: Large Language Models Collaborating on Long-Context Tasks](https://mlsanity.com/paper/2406.02818/) (2024)
- [Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation](https://mlsanity.com/paper/2212.07981/) (2022)
- [Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors](https://mlsanity.com/paper/2205.12854/) (2022)
- [From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting](https://mlsanity.com/paper/2309.04269/) (2023)
- [Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization](https://mlsanity.com/paper/2108.13684/) (2021)
- [PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter](https://mlsanity.com/paper/2310.18347/) (2023)
- [Conversational Information Seeking](https://mlsanity.com/paper/2201.08808/) (2022)
- [SEMQA: Semi-Extractive Multi-Source Question Answering](https://mlsanity.com/paper/2311.04886/) (2023)
- [Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers](https://mlsanity.com/paper/2403.01061/) (2024)
- [Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries](https://mlsanity.com/paper/2109.09195/) (2021)
- [Evaluating Generative Ad Hoc Information Retrieval](https://mlsanity.com/paper/2311.04694/) (2023)
- [SWAN: A Generic Framework for Auditing Textual Conversational Systems](https://mlsanity.com/paper/2305.08290/) (2023)

## Built on by (most cited first, 16 of 16)

- [Auto-ARGUE: LLM-Based Report Generation Evaluation](https://mlsanity.com/paper/2509.26184/) (2025)
- [Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.20825/) (2025)
- [An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs](https://mlsanity.com/paper/2508.21024/) (2025)
- [Incorporating Q&A Nuggets into Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.13222/) (2026)
- [LANCER: LLM Reranking for Nugget Coverage](https://mlsanity.com/paper/2601.22008/) (2026)
- [UiS-IAI@LiveRAG: Retrieval-Augmented Information Nugget-Based Generation of Responses](https://mlsanity.com/paper/2506.22210/) (2025)
- [RoutIR: Fast Serving of Retrieval Pipelines for Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.10644/) (2026)
- [Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?](https://mlsanity.com/paper/2601.13227/) (2026)
- [Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage](https://mlsanity.com/paper/2603.08819/) (2026)
- [DR3-Eval: Towards Realistic and Reproducible Deep Research Evaluation](https://mlsanity.com/paper/2604.14683/) (2026)
- [NuggetIndex: Governed Atomic Retrieval for Maintainable RAG](https://mlsanity.com/paper/2604.27306/) (2026)
- [DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation](https://mlsanity.com/paper/2605.04458/) (2026)
- [MARQUIS: A Three-Stage Pipeline for Video Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.17640/) (2026)
- [Plans for Evaluating Structured Generative Search Summaries](https://mlsanity.com/paper/2605.26400/) (2026)
- [CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG](https://mlsanity.com/paper/2608.07458/) (2026)
- [MultiVENT-Raw: A Benchmark for Retrieval and Reasoning over Raw Videos](https://mlsanity.com/paper/2609.28437/) (2026)

## 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.