The power of noise: Redefining retrieval for rag systems
Open accessAbstract
Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with relevant passages or documents retrieved by an Information Retrieval (IR) system. RAG has become increasingly important for Generative AI solutions, especially in enterprise settings or in any domain in which knowledge is constantly refreshed and cannot be memorized in the LLM. We argue here that the retrieval component of RAG systems, be it dense or sparse, deserves increased attention from the research community, and accordingly, we conduct the first comprehensive and systematic examination of the retrieval strategy of RAG systems. We focus, in particular, on the type of passages IR systems within a RAG solution should retrieve. Our analysis considers multiple factors, such as the relevance of the passages included in the prompt context, their position, and their number. One counter-intuitive finding of this work is that the retriever's highest-scoring documents that are not directly relevant to the query (e.g., do not contain the answer) negatively impact the effectiveness of the LLM. Even more surprising, we discovered that adding random documents in the prompt improves the LLM accuracy by up to 35%. These results highlight the need to investigate the appropriate strategies when integrating retrieval with LLMs, thereby laying the groundwork for future research in this area.
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Overly-long texts can introduce noise, potentially obscuring specific facts.
“However, overly-long texts can introduce noise (Yang et al., 2023; Chen et al., 2023; Cuconasu et al., 2024; Zhang et al., 2023e), potentially obscuring specific facts (Trivedi et al., 2022; Yu et al., 2023b).”
From The AI Hippocampus: How Far are We From Human Memory? (2026) · cites this paper as Cuconasu et al., 2024
Section 5.1 (p. 5) finds that adding more distracting, answer-free documents to a RAG context progressively reduces accuracy, supporting the possibility that extra text can interfere with finding an answer. It does not test whether overly long texts themselves cause this effect; Section 5.3 (p. 6) also reports that adding random documents as noise generally did not reduce performance. Cuconasu et al. is cited for the first clause, not the separate claim about obscuring specific facts. (LLM, conf=0.83)
Experimental — accuracy not yet measuredWhen the generator is not robust enough to irrelevant retrievals, it will mislead the generator and even introduce hallucinations.
“When the generator is not robust enough to these irrelevant retrievals, it will mislead the generator and even introduce hallucinations [65].”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [65]
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Cites
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575 · see citing works
The probabilistic relevance framework: BM25 and beyond
Robertson, S., & Zaragoza, H. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000019 · Cited by 3,374 · see citing works
Natural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069 · see citing works
Lost in the Middle: How Language Models Use Long Contexts
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00638 · Cited by 1,612 · see citing works
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
Khattab, O., & Zaharia, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401075 · Cited by 1,301 · see citing works
The Falcon Series of Open Language ModelsPreprint
Almazrouei, E., Alobeidli, H., AlShamsi, A., Cappelli, A., Cojocaru, R.-A., Debbah, M., Goffinet, É., Hesslow, D., Launay, J., Malartic, Q., et al. (2023) · arXiv.org · doi:10.48550/arxiv.2311.16867 · Cited by 813 · see citing works
Recipes for Building an Open-Domain Chatbot
Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E. M., Boureau, Y.-L., et al. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.24 · Cited by 784 · see citing works
Latent retrieval for weakly supervised open domain question answering
Lee, K., Chang, M.-W., & Toutanova, K. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1612 · Cited by 771 · see citing works
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
Lu, Y., Bartolo, M., Moore, A., Riedel, S., & Stenetorp, P. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.556 · Cited by 610 · see citing works
Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., & Jégou, H. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3618474 · Cited by 478 · see citing works
In-Context Retrieval-Augmented Language Models
Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00605 · Cited by 449 · see citing works
Sharp nearby, fuzzy far away: How neural language models use context
Khandelwal, U., He, H., Qi, P., & Jurafsky, D. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1027 · Cited by 303 · see citing works
Optimizing Dense Retrieval Model Training with Hard Negatives
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · arXiv · doi:10.48550/arxiv.2104.08051 · Cited by 251 · see citing works
Optimizing Dense Retrieval Model Training with Hard Negatives
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462880 · Cited by 248 · see citing works
AmbigQA: Answering Ambiguous Open-domain Questions
Min, S., Michael, J., Hajishirzi, H., & Zettlemoyer, L. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.466 · Cited by 219 · see citing works
Penedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., Alobeidli, H., Pannier, B., Almazrouei, E., & Launay, J. (2023) · arXiv · doi:10.48550/arxiv.2306.01116 · Cited by 164 · see citing works
Asai, A., Wu, Z., Wang, Y., Sil, A., & Hajishirzi, H. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.11511 · Cited by 101 · see citing works
Fast Transformer Decoding: One Write-Head is All You Need
Shazeer, N. (2019) · arXiv · doi:10.48550/arxiv.1911.02150 · Cited by 101 · see citing works
TL;DR: Mining Reddit to Learn Automatic Summarization
Völske, M., Potthast, M., Syed, S., & Stein, B. (2017) · Workshop on New Frontiers in Summarization (NewSum) · doi:10.18653/v1/w17-4508 · Cited by 94 · see citing works
Bridging the Preference Gap between Retrievers and LLMsPreprint
Ke, Z.-X., Kong, W., Li, C., et al. (2024) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2401.06954 · Cited by 94 · see citing works
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Douze, M., Guzhva, A., Deng, C., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.08281 · Cited by 75 · see citing works
Dr ChatGPT tell me what I want to hear: How different prompts impact health answer correctness
Koopman, B., & Zuccon, G. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.928 · Cited by 73 · cited 1× · see citing works
“In contrast, dense retrievers, emerging from advancements in deep learning, utilize low-dimensional dense vectors for representation.”
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Cui, Y., Yang, Z., & Yao, X. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.08177 · Cited by 71 · see citing works
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Xie, T., Zhang, D., Chen, J., Li, X., Zhao, S., Cao, R., Hua, T. J., Cheng, Z., Shin, D., Lei, F., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.07972 · Cited by 64 · see citing works
(2021) · doi:10.18653/v1/2021.naacl-tutorials.1 · Cited by 59 · see citing works
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Zuccon, G., Koopman, B., & Shaik, R. S. (2023) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3624918.3625329 · Cited by 56 · see citing works
Do Long-Range Language Models Actually Use Long-Range Context?
Sun, S., Krishna, K., Mattarella-Micke, A., & Iyyer, M. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.62 · Cited by 43 · see citing works
BehnamGhader, P., Miret, S., & Reddy, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1036 · Cited by 15 · see citing works
On the Role of Relevance in Natural Language Processing Tasks
Sauchuk, A., Thorne, J. P., Halevy, A. Y., Tonellotto, N., & Silvestri, F. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3532034 · Cited by 14 · see citing works
Self-citationTrappolini, G., Santilli, A., Rodolà, E., Halevy, A. Y., & Silvestri, F. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591930 · Cited by 11 · see citing works
Self-citationTolomei, G., Campagnano, C., Silvestri, F., & Trappolini, G. (2023) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi58952.2023.00027 · Cited by 9 · see citing works
Self-citationCamoscio: an Italian Instruction-tuned LLaMAPreprint
Santilli, A., & Rodolà, E. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.16456 · Cited by 3 · see citing works
OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models
Wang, S., Ding, L., Shen, L., Luo, Y., Du, B., & Tao, D. (2024) · arXiv · doi:10.48550/arxiv.2401.06628
Flagged first, then by citations
Research works printed but not linked: 15
- [4][4] Andrea Bacciu, Florin Cuconasu, Federico Siciliano, Fabrizio Silvestri, Nicola Tonellotto, and Giovanni Trappolini. 2023. RRAML: Reinforced Retrieval Augmented Machine Learning. In Proceedings of the Discussion Papers - 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023 DP) co-located with 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023), Rome, Italy, November 6-9, 2023 (CEUR Workshop Proceedings, Vol. 3537) , Roberto Basili, Domenico Lembo, Carla Limongelli, and Andrea Orlandini (Eds.). CEUR-WS.org, 29-37. https://ceur-ws.org/Vol-3537/paper4.pdf -
- [5][5] Andrea Bacciu, Giovanni Trappolini, Andrea Santilli, Emanuele Rodolà, and Fabrizio Silvestri. 2023. Fauno: The Italian Large Language Model that will leave you senza parole!. In Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023), Pisa, Italy, June 8-9, 2023 (CEUR Workshop Proceedings, Vol. 3448) , Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara (Eds.). CEUR-WS.org, 9-17. https://ceur-ws.org/Vol-3448/paper-24.pdf -
- [8][8] Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. 2022. Improving language models by retrieving from trillions of tokens. In International conference on machine learning . PMLR, Baltimora, 2206-2240. -
- [9][9] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877-1901. -
- [13][13] Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020. Retrieval augmented language model pre-training. In International conference on machine learning . PMLR, Vienna, 3929-3938. -
- [18][18] Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023. Large language models struggle to learn long-tail knowledge. In Proceedings of the 40th International Conference on Machine Learning (ICML'23) . JMLR.org, Honolulu, Hawaii, USA, Article 641, 12 pages. -
- [22][22] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT , Vol. 1. Association for Computational Linguistic, Minneapolis, 2. -
- [28][28] Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459-9474. -
- [32][32] C Manning, P Raghavan, and H Schutze. 2008. Term weighting, and the vector space model . Cambridge University Press Cambridge, Cambridge. 109-133 pages. -
- [36][36] Ofir Press, Noah Smith, and Mike Lewis. 2022. Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation. https://openreview.net/ forum?id=R8sQPpGCv0 -
- [38][38] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9. -
- [42][42] Gerard Salton and Michael J. McGill. 1983. Introduction to modern information retrieval. McGraw-Hill (1983). -
- [52][52] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc., Long Beach. https://proceedings.neurips.cc/ paper\_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf -
- [55][55] Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2023. Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts. In The Twelfth International Conference on Learning Representations . -
- [58][58] Shuangfei Zhai, Tatiana Likhomanenko, Etai Littwin, Dan Busbridge, Jason Ramapuram, Yizhe Zhang, Jiatao Gu, and Joshua M Susskind. 2023. Stabilizing transformer training by preventing attention entropy collapse. In International Conference on Machine Learning . PMLR, PMLR, Hawaii, 40770-40803. -
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Guo, P., Xue, H., Ma, J., & Cheng, J. C. P. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106374 · Cited by 31 · see citing works
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Agarwal, S., Sundaresan, S., Mitra, S., Mahapatra, D., Gupta, A., Sharma, R., Kapu, N. J., Yu, T., & Saini, S. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725273 · Cited by 21 · see citing works
“Contrary to the popular belief that decode is slow in LLMs, for RAG-systems prefill phase typically dominates both the amount of token computation and total latency, despite being highly parallelized.”
A Survey of Context Engineering for Large Language Models
Mei, L., Yao, J., Ge, Y., Wang, Y., Bi, B., Cai, Y., Liu, J., Li, M., Li, Z.-Z., Zhang, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.13334 · Cited by 13 · see citing works
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Gao, Y., Xiong, Y., Wu, W., Li, B., Zhong, Y., & Wang, H. (2026) · ACM Transactions on Information Systems · doi:10.1145/3786609 · Cited by 6 · see citing works
“Thereafter, in response to the afore-mentioned research questions, we will provide a detailed introduction to the core dimensions and settings within the framework.”
Ipa, A. S., Rony, M. A. T., & Islam, M. S. (2025) · Proceedings of the 1st Workshop on Language Models for Underserved Communities (LM4UC 2025) · doi:10.18653/v1/2025.lm4uc-1.2 · Cited by 3 · see citing works
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Jia, Z., Li, J., Kang, Y., Wang, Y., Wu, T., Wang, Q., Wang, X., Zhang, S., Shen, J., Li, Q., et al. (2026) · arXiv · doi:10.48550/arxiv.2601.09113
- Authors
- Cuconasu, F., Trappolini, G., Siciliano, F., Filice, S., Campagnano, C., Maarek, Y., Tonellotto, N., & Silvestri, F.
- Affiliations
- Sapienza University of Rome (IT)
- Technology Innovation Institute, Haifa, Israel
- University of Pisa (IT)
- ORCID
- Venue
- Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2024-07-10
- Pages
- 719-729
- OpenAlex
- W4391376033
- Cited by
- 236
- References
- 33
- Citations by year
- 2026: 95 · 2025: 118 · 2024: 23
- FWCI
- 69.64
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Centro Nazionale di Ricerca in High-Performance Computing, Big Data and Quantum Computing (CN00000013)
- PNRR (PE00000013,PE00000014,IR0000013,M4C2)
- Italian Ministry of Education and Research (2022AEFHAZ)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- retrieval-augmented generation, information retrieval, large language models, dense retrieval, sparse retrieval, retrieval strategies
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