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

arXiv: https://arxiv.org/abs/2411.14572 · HTML: https://mlsanity.com/paper/2411.14572/

## 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.

## 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)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://mlsanity.com/paper/2310.01405/) (2023)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (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)
- [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://mlsanity.com/paper/2403.14403/) (2024)
- [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://mlsanity.com/paper/2310.01558/) (2023)
- [Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts](https://mlsanity.com/paper/2305.13300/) (2023)
- [The Power of Noise: Redefining Retrieval for RAG Systems](https://mlsanity.com/paper/2401.14887/) (2024)
- [The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)](https://mlsanity.com/paper/2402.16893/) (2024)
- [Retrieval Augmented Code Generation and Summarization](https://mlsanity.com/paper/2108.11601/) (2021)
- [Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation](https://mlsanity.com/paper/2307.11019/) (2023)
- [LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain](https://mlsanity.com/paper/2408.10343/) (2024)
- [Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training](https://mlsanity.com/paper/2405.20978/) (2024)
- [Pandora: Jailbreak GPTs by Retrieval Augmented Generation Poisoning](https://mlsanity.com/paper/2402.08416/) (2024)
- [Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis](https://mlsanity.com/paper/2406.10794/) (2024)
- [When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation](https://mlsanity.com/paper/2402.11457/) (2024)
- [RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback](https://mlsanity.com/paper/2403.06840/) (2024)
- [Towards the Effect of Examples on In-Context Learning: A Theoretical Case Study](https://mlsanity.com/paper/2410.09411/) (2024)

## Built on by (most cited first, 20 of 20)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Empowering GraphRAG with Knowledge Filtering and Integration](https://mlsanity.com/paper/2503.13804/) (2025)
- [DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router](https://mlsanity.com/paper/2507.22050/) (2025)
- [Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach](https://mlsanity.com/paper/2502.14100/) (2025)
- [To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems](https://mlsanity.com/paper/2506.02546/) (2025)
- [Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.13957/) (2025)
- [Continuously Steering LLMs Sensitivity to Contextual Knowledge with Proxy Models](https://mlsanity.com/paper/2508.19720/) (2025)
- [Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation](https://mlsanity.com/paper/2506.20869/) (2025)
- [Retrieval-Augmented Generation for Service Discovery: Chunking Strategies and Benchmarking](https://mlsanity.com/paper/2505.19310/) (2025)
- [Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.12460/) (2025)
- [Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions](https://mlsanity.com/paper/2604.08304/) (2026)
- [Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs](https://mlsanity.com/paper/2602.07778/) (2026)
- [UsefulBench: Towards Decision-Useful Information as a Target for Information Retrieval](https://mlsanity.com/paper/2604.15827/) (2026)
- [Magnifying What Matters: Attention-Guided Adaptive Rendering for Visual Text Comprehension](https://mlsanity.com/paper/2606.12898/) (2026)
- [On the Diminishing Returns of Complex Robust RAG Training in the Era of Powerful LLMs](https://mlsanity.com/paper/2502.11400/) (2025)
- [A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM](https://mlsanity.com/paper/2604.06666/) (2026)
- [Why Retrieval-Augmented Generation Fails: A Graph Perspective](https://mlsanity.com/paper/2605.14192/) (2026)
- [Retrieval-Augmented Generation to Support Railways Engineering Tasks: A Case Study](https://mlsanity.com/paper/2607.01244/) (2026)
- [When Specifications Conflict: A Symmetry-Based Framework for Measuring LLM Preferences](https://mlsanity.com/paper/2607.28384/) (2026)
- [Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generation](https://mlsanity.com/paper/2609.30337/) (2026)

## 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.