---
title: "Who cites The Power of Noise: Redefining — 97 citing papers"
description: "97 papers of the Elamur Research library cite «The Power of Noise: Redefining Retrieval for RAG Systems», found in their bibliographies; the…"
canonical: "https://research.elamur.ai/free/en/papers/cuconasu-2024-power-noise-redefining-retrieval-rag/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [The Power of Noise: Redefining Retrieval for RAG…](https://md.research.elamur.ai/free/en/papers/cuconasu-2024-power-noise-redefining-retrieval-rag/) / cited by

# Who cites «The Power of Noise: Redefining Retrieval for RAG Systems»

97 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · [← back to «The Power of Noise: Redefining Retrieval for RAG Systems»](https://md.research.elamur.ai/free/en/papers/cuconasu-2024-power-noise-redefining-retrieval-rag/)

1. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
2. [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,663 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
3. [**Retrieval-Augmented Generation for AI-Generated Content: A Survey arXiv:2402.19473**Penghao Zhao, Hailin Zhang, Qinhan Yu, et al.*2024 · 301 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2024-retrieval-augmented-generation-ai-generated/)
4. [**Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools arXiv:2405.20362**Varun Magesh, Faiz Surani, Matthew Dahl, et al.*2024 · 271 citations*](https://md.research.elamur.ai/free/en/papers/magesh-2024-hallucination-free-assessing-reliability-leading/)
5. **[Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-astute-rag-overcoming-imperfect-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fei Wang, Xingchen Wan, Ruoxi Sun, et al.*2025 · 59 citations*
6. **[REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/wang-2024-rear-relevance-aware-retrieval-augmented/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuhao Wang, Ruiyang Ren, Junyi Li, et al.*2024 · 55 citations*
7. **[Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-stable-rag-mitigating-retrieval-permutation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qianchi Zhang, Hainan Zhang, Liang Pang, et al.*2026 · 27 citations*
8. **[TC–RAG: Turing–Complete RAG's Case study on Medical LLM Systems](https://md.research.elamur.ai/free/en/papers/jiang-2025-tc-rag-turing-complete-rag/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinke Jiang, Yue Fang, Rihong Qiu, et al.*2025 · 25 citations*
9. **[The Distracting Effect: Understanding Irrelevant Passages in RAG](https://md.research.elamur.ai/free/en/papers/amiraz-2025-distracting-effect-understanding-irrelevant-passages/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chen Amiraz, Florin Cuconasu, Simone Filice, et al.*2025 · 21 citations*
10. **[Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-pandora-s-box-or-aladdin/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinyang Wu, Shuai Zhang, Feihu Che, et al.*2025 · 19 citations*
11. **[LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/wang-2025-llms-know-what-they-need/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Keheng Wang, Feiyu Duan, Peiguang Li, et al.*2025 · 14 citations*
12. [**How Much Can RAG Help the Reasoning of LLM? arXiv:2410.02338**Jingyu Liu, Jiaen Lin, Yong Liu*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-much-rag-help-reasoning-llm/)
13. **[SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence](https://md.research.elamur.ai/free/en/papers/liu-2025-selfelicit-your-language-model-secretly/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhining Liu, Rana Ali Amjad, Ravinarayana Adkathimar, et al.*2025 · 11 citations*
14. **[Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach](https://md.research.elamur.ai/free/en/papers/zeng-2025-context-robust-llms-gated-representation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shenglai Zeng, Pengfei He, Kai Guo, et al.*2025 · 5 citations*
15. **[Test-time Corpus Feedback: From Retrieval to RAG](https://md.research.elamur.ai/free/en/papers/rathee-2026-test-time-corpus-feedback-retrieval/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mandeep Rathee, Venktesh V, Sean MacAvaney, et al.*2026 · 4 citations*
16. **[QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/min-2026-quco-rag-quantifying-uncertainty-pre/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dehai Min, Kailin Zhang, Tongtong Wu, et al.*2026 · 2 citations*
17. **[ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling](https://md.research.elamur.ai/free/en/papers/xu-2026-thinknote-enhancing-knowledge-integration-utilization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhipeng Xu, Zhenghao Liu, Yukun Yan, et al.*2026 · 2 citations*
18. **[Knowing When Not to Answer: Lightweight KB-Aligned OOD Detection for Safe RAG](https://md.research.elamur.ai/free/en/papers/triantafyllopoulos-2026-knowing-when-not-answer-lightweight/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Triantafyllopoulos, Renyi Qu, Salvatore Giorgi, et al.*2026 · 1 citation*
19. **[Redefining Retrieval Evaluation in the Era of LLMs](https://md.research.elamur.ai/free/en/papers/trappolini-2026-redefining-retrieval-evaluation-era-llms/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Giovanni Trappolini, Florin Cuconasu, Simone Filice, et al.*2026 · 1 citation*
20. **[ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue](https://md.research.elamur.ai/free/en/papers/shen-2026-acr-adaptive-context-refactoring-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiawei Shen, Jia Zhu, Hanghui Guo, et al.*2026*
21. **[Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering](https://md.research.elamur.ai/free/en/papers/nishida-2026-dissecting-graphrag-modular-analysis-knowledge/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Noriki Nishida, Rumana Ferdous Munne, Shanshan Liu, et al.*2026*
22. **[MODE-RAG: Manifold Outlier Diagnosis and Energy-based Retrieval-Augmented Generation Evaluation](https://md.research.elamur.ai/free/en/papers/wei-2026-mode-rag-manifold-outlier-diagnosis/) [Workshop on Multimodal Augmented Generation via Multimodal Retrieval](https://md.research.elamur.ai/free/en/venues/magmar/)**Zehang Wei, JiaXin Dai, Jiamin Yan, et al.*2026*
23. **[Structured Episodic Event Memory](https://md.research.elamur.ai/free/en/papers/lu-2026-structured-episodic-event-memory/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhengxuan Lu, Dongfang Li, Yukun Shi, et al.*2026*
24. **[Uncovering Ideological Bias in RAG with Lexical Multidimensional Analysis: A Case Study on COVID-19](https://md.research.elamur.ai/free/en/papers/salari-2026-uncovering-ideological-bias-rag-lexical/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Elmira Salari, Maria Claudia Nunes Delfino, Hazem Amamou, et al.*2026*

73 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/cuconasu-2024-power-noise-redefining-retrieval-rag/) — The Power of Noise: Redefining Retrieval for RAG Systems