mlsanityData to 14 Oct 2026

Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective

arXiv 2411.14572 ↗First posted 21 Nov 2024cs.LG cs.CLcited by 20

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively integrating external knowledge with the LLM's internal knowledge, often leading to issues with misleading or unhelpful information. This work aims to provide a systematic study on knowledge checking in RAG systems. We conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking. Motivated by the findings, we further develop representation-based classifiers for knowledge filtering. We show substantial improvements in RAG performance, even when dealing with noisy knowledge databases. Our study provides new insights into leveraging LLM representations for enhancing the reliability and effectiveness of RAG systems.

Key facts. Cited by 20 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 39 references that are in the map. First posted on arXiv 21 Nov 2024.

Builds on

23 of 39 references.

Closest ancestors · 1

Same direction, newest first

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

Other references · 22

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2022Locating and Editing Factual Associations in GPTcited by 2,691
  4. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  5. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,336
  6. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  7. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  8. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  9. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  10. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  11. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  12. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 341
10 more
  1. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
  2. 2021Retrieval Augmented Code Generation and Summarizationcited by 149
  3. 2023Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentationcited by 147
  4. 2024LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domaincited by 118
  5. 2024Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Trainingcited by 89
  6. 2024Pandora: Jailbreak GPTs by Retrieval Augmented Generation Poisoningcited by 64
  7. 2024Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysiscited by 64
  8. 2024When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentationcited by 59
  9. 2024RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedbackcited by 46
  10. 2024Towards the Effect of Examples on In-Context Learning: A Theoretical Case Studycited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 10; 2026: 9.

20 papers.

Sort:

By area:

  1. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 172
  2. 2025Empowering GraphRAG with Knowledge Filtering and Integrationcited by 15
  3. 2025DeepSieve: Information Sieving via LLM-as-a-Knowledge-Routercited by 13
  4. 2025Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approachcited by 6
  5. 2025To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systemscited by 6
  6. 2025Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generationcited by 5
  7. 2025Continuously Steering LLMs Sensitivity to Contextual Knowledge with Proxy Modelscited by 5
  8. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluationcited by 3
  9. 2025Retrieval-Augmented Generation for Service Discovery: Chunking Strategies and Benchmarkingcited by 2
  10. 2025Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generationcited by 2
  11. 2026Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directionscited by 2
  12. 2026Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMscited by 1
8 more
  1. 2026UsefulBench: Towards Decision-Useful Information as a Target for Information Retrievalcited by 1
  2. 2026Magnifying What Matters: Attention-Guided Adaptive Rendering for Visual Text Comprehensioncited by 1
  3. 2025On the Diminishing Returns of Complex Robust RAG Training in the Era of Powerful LLMscited by 0
  4. 2026A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLMcited by 0
  5. 2026Why Retrieval-Augmented Generation Fails: A Graph Perspectivecited by 0
  6. 2026Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Studycited by 0
  7. 2026When Specifications Conflict: A Symmetry-Based Framework for Measuring LLM Preferencescited by 0
  8. 2026Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey
  2. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  3. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  4. 2024On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models
  5. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  6. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  7. 2025Dynamic and Parametric Retrieval-Augmented Generation
  8. 2025PRGB Benchmark: A Robust Placeholder-Assisted Algorithm for Benchmarking Retrieval-Augmented Generation