mlsanityData to 7 Oct 2026

Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval

arXiv 2410.13339 ↗First posted 17 Oct 2024cs.CLcited by 28

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) enhances language models by retrieving and incorporating relevant external knowledge. However, traditional retrieve-and-generate processes may not be optimized for real-world scenarios, where queries might require multiple retrieval steps or none at all. In this paper, we propose a Probing-RAG, which utilizes the hidden state representations from the intermediate layers of language models to adaptively determine the necessity of additional retrievals for a given query. By employing a pre-trained prober, Probing-RAG effectively captures the model's internal cognition, enabling reliable decision-making about retrieving external documents. Experimental results across five open-domain QA datasets demonstrate that Probing-RAG outperforms previous methods while reducing the number of redundant retrieval steps.

Key facts. Cited by 28 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 24 of its 37 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

24 of 37 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory3 May

Other references · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  5. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  6. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  7. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  8. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  9. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  10. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  11. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  12. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
11 more
  1. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  2. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  3. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  4. 2023RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generationcited by 357
  5. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  6. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 333
  7. 2024Knowledge Conflicts for LLMs: A Surveycited by 274
  8. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  9. 2024Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Mapscited by 140
  10. 2021End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answeringcited by 133
  11. 2024Factual Confidence of LLMs: on Reliability and Robustness of Current Estimatorscited by 59

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 12; 2026: 16.

28 papers.

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  1. 2025Parametric Retrieval Augmented Generationcited by 38
  2. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 34
  3. 2025What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyondcited by 22
  4. 2025DeepSieve: Information Sieving via LLM-as-a-Knowledge-Routercited by 13
  5. 2025Stop-RAG: Value-Based Retrieval Control for Iterative RAGcited by 13
  6. 2025Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Controlcited by 6
  7. 2026LLMs Should Express Uncertainty Explicitlycited by 6
  8. 2025Conflict-Aware Soft Prompting for Retrieval-Augmented Generationcited by 5
  9. 2025MASH: Modeling Abstention via Selective Help-Seekingcited by 3
  10. 2026Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routingcited by 3
  11. 2025Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMscited by 2
  12. 2025Retrieval Feedback Memory Enhancement Large Model Retrieval Generation Methodcited by 2
16 more
  1. 2025DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Searchcited by 2
  2. 2026Lightweight Query Routing for Adaptive RAG: A Baseline Study on RAGRouter-Benchcited by 2
  3. 2026When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answeringcited by 1
  4. 2026Return or Revise? Learning When Revision Helps Retrieval-Augmented QAcited by 1
  5. 2025Iterative Tree Analysis for Medical Criticscited by 0
  6. 2026ExDR: Explanation-driven Dynamic Retrieval Enhancement for Multimodal Fake News Detectioncited by 0
  7. 2026Is a Picture Worth a Thousand Words? Adaptive Multimodal Fact-Checking with Visual Evidence Necessitycited by 0
  8. 2026RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigmcited by 0
  9. 2026A Triple-Robustness Analysis of Retrieval-Augmented Generation for Multi-Hop Requirements Traceabilitycited by 0
  10. 2026Universal Pathologies, Conditional Consequences: A Triple-Robustness Analysis of RAG for Multi-Hop Traceabilitycited by 0
  11. 2026Knowing Before Answering: Decoding Language Models for Reliable RAGcited by 0
  12. 2026On-Demand Attention: Language Models Know When to Recallcited by 0
  13. 2026Memory Control Signals Emerge Before Action in Long Horizon Agentscited by 0
  14. 2026Before Answering: Evidence Sufficiency under Size-Matched Memory Constructioncited by 0
  15. 2026Relevance Is Not Sufficient Evidence: Detecting Evidence Gaps Before Generation in RAGcited by 0
  16. 2026Locating Answer-Correctness Signals in Frozen Large Language Modelscited by 0

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  2. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
  3. 2024Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
  4. 2024Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
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  8. 2025Insight-RAG: Enhancing LLMs with Insight-Driven Augmentation