mlsanityData to 7 Oct 2026

DeepNote: Note-Centric Deep Retrieval-Augmented Generation

arXiv 2410.08821 ↗First posted 11 Oct 2024cs.CLcited by 8

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) mitigates factual errors and hallucinations in Large Language Models (LLMs) for question-answering (QA) by incorporating external knowledge. However, existing adaptive RAG methods rely on LLMs to predict retrieval timing and directly use retrieved information for generation, often failing to reflect real information needs and fully leverage retrieved knowledge. We develop DeepNote, an adaptive RAG framework that achieves in-depth and robust exploration of knowledge sources through note-centric adaptive retrieval. DeepNote employs notes as carriers for refining and accumulating knowledge. During in-depth exploration, it uses these notes to determine retrieval timing, formulate retrieval queries, and iteratively assess knowledge growth, ultimately leveraging the best note for answer generation. Extensive experiments and analyses demonstrate that DeepNote significantly outperforms all baselines (+10.2% to +20.1%) and exhibits the ability to gather knowledge with both high density and quality. Additionally, DPO further improves the performance of DeepNote. The code and data are available at https://github.com/thunlp/DeepNote.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 35 of its 44 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

35 of 44 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Bridging the Preference Gap between Retrievers and LLMs13 Jan

Other references · 34

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  5. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  8. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  9. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  10. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  11. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  12. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
22 more
  1. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  2. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  3. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  4. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  5. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  6. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  7. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
  8. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  9. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  10. 2023Enabling Large Language Models to Generate Text with Citationscited by 584
  11. 2021Large Dual Encoders Are Generalizable Retrieverscited by 454
  12. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 452
  13. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  14. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 231
  15. 2023RA-DIT: Retrieval-Augmented Dual Instruction Tuningcited by 194
  16. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 180
  17. 2022Rethinking with Retrieval: Faithful Large Language Model Inferencecited by 145
  18. 2023Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answeringcited by 119
  19. 2024RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculturecited by 80
  20. 2024RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generationcited by 75
  21. 2024Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Modelscited by 44
  22. 2020AVA: an Automatic eValuation Approach to Question Answering Systemscited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025Synergizing RAG and Reasoning: A Systematic Reviewcited by 27
  2. 2025HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modelingcited by 4
  3. 2026When Knowledge Is Not Free: Cost-Aware Evidence Selection in Retrieval-Augmented Generationcited by 3
  4. 2026Finding What Matters: Anchoring Context Knowledge with Evolving Indices for Iterative Retrievalcited by 2
  5. 2026Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Modelscited by 2
  6. 2025ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generationcited by 1
  7. 2026SpecHop: Continuous Speculation for Accelerating Multi-Hop Retrieval Agentscited by 1
  8. 2026ConCise: Training-Free Conclusion-Chain State Compression for Cost-Efficient Multi-Step RAG Servicescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  2. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  3. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models
  4. 2024Long2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall
  5. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  6. 2024Searching for Best Practices in Retrieval-Augmented Generation
  7. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  8. 2024Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems