Elamur Research⌕
☰
home / papers / 2024 / The Power of Noise: Redefining Retrieval for RAG Systems

The Power of Noise: Redefining Retrieval for RAG Systems

Paper · 2024 · Florin Cuconasu, Giovanni Trappolini, Federico Siciliano et al.

Summary

Retrieval-Augmented Generation (RAG) supplies an LLM with passages from an IR system so it can answer questions beyond its pre-trained knowledge. Research has mostly focused on the generator, so this paper examines the retriever and asks which passages should go into the prompt. The authors frame RAG as open-domain question answering and sort candidate passages into three types: relevant passages, distracting passages that are on-topic but lack the answer, and random passages that have nothing to do with the query. They then vary the type, number and position of passages in the prompt for models such as Llama2-7b and Falcon-7b, using retrievers like Contriever.

The results run against common assumptions. Distracting passages that the retriever scores highly lower answer accuracy. Unrelated noise can raise accuracy, provided the prompt is arranged correctly. The study argues that retrievers built for human search may not suit LLM consumers, and that IR methods should be designed with the generator in mind.

Key points

  • Adding random documents to the prompt improved LLM accuracy by up to 35%.
  • Relevant (gold) passages work best when placed close to the query. When they sit farther away, the model has trouble using them.
  • The gains from noise held for random text from a very different corpus, Reddit Webis-TLDR-17, and also for nonsensical strings of random words. This was shown in experiments combining Llama2-7b and Contriever, with Wilcoxon significance tests.
  • A possible explanation is that random documents raise attention entropy by about 3X, which may help avoid "entropy collapse". The authors say this explanation is not yet conclusive.
  • The paper appeared at SIGIR '24. Code and data are available at github.com/florin-git/The-Power-of-Noise.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Sapienza University of RomeRome · Italy57 works · 9,564 citations🎓University of PisaPisa · Italy25 works · 3,842 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 473 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Retrieval-augmented generation · 50 works

Cite

Cite as Florin Cuconasu, Giovanni Trappolini, Federico Siciliano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto and Fabrizio Silvestri. 2024. The Power of Noise: Redefining Retrieval for RAG Systems. arXiv:2401.14887.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1145/3626772.3657834 · arXiv 2401.14887

@misc{cuconasu2024power,
  title = {{The Power of Noise: Redefining Retrieval for RAG Systems}},
  author = {Florin Cuconasu, Giovanni Trappolini, Federico Siciliano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto and Fabrizio Silvestri},
  year = {2024},
  doi = {10.1145/3626772.3657834},
  eprint = {2401.14887},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3626772.3657834}
}

References

60 references, 29 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Mérouane Debbah, Étienne Goffinet, Daniel Hesslow, Julien Launay, Quentin Malartic, Daniele Mazzotta, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. The Falcon Series of Open Language Mo…
  2. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. arXiv:2310.11511 [cs.CL]
  3. Giuseppe Attanasio, Debora Nozza, Dirk Hovy, and Elena Baralis. 2022. Entropy-based attention regularization frees unintended bias mitigation from lists. find
  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 Intellig… find
  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 Proc… find
  6. Edward Beeching, Clémentine Fourrier, Nathan Habib, Sheon Han, Nathan Lambert, Nazneen Rajani, Omar Sanseviero, Lewis Tunstall, and Thomas Wolf. 2023. Open LLM Leaderboard. https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard. ↗
  7. Parishad BehnamGhader, Santiago Miret, and Siva Reddy. 2023. Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model. In Findings of the Association for Computational Linguistics: EMNLP 2023, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Associ… find
  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 learn… find
  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.
  10. Yiming Cui, Ziqing Yang, and Xin Yao. 2023. Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca. arXiv preprint arXiv:2304.08177 (2023). https://arxiv.org/abs/2304.08177 ↗
  11. Matthijs Douze, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazaré, Maria Lomeli, Lucas Hosseini, and Hervé Jégou. 2024. The Faiss library. (2024). arXiv:2401.08281 [cs.LG]
  12. Garrachonr. 2023. LlamaDos. https://github.com/Garrachonr/LlamaDos. ↗
  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.
  14. David T Hoffmann, Simon Schrodi, Nadine Behrmann, Volker Fischer, and Thomas Brox. 2023. Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization Problems. find
  15. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021. Unsupervised dense information retrieval with contrastive learning.
  16. Mojan Javaheripi, Sébastien Bubeck, Marah Abdin, Jyoti Aneja, Sebastien Bubeck, Caio César Teodoro Mendes, Weizhu Chen, Allie Del Giorno, Ronen Eldan, Sivakanth Gopi, et al. 2023. Phi-2: The surprising power of small language models. find
  17. jphme. 2023. Llama-2-13b-chat-german. https://huggingface.co/jphme/Llama-2-13b-chat-german. ↗
  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.
  19. Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for open-domain question answering.
  20. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Bonnie Webber, Trevor…
  21. Zixuan Ke, Weize Kong, Cheng Li, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. 2024. Bridging the Preference Gap between Retrievers and LLMs. arXiv preprint arXiv:2401.06954 (2024).
  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.
  23. Urvashi Khandelwal, He He, Peng Qi, and Dan Jurafsky. 2018. Sharp nearby, fuzzy far away: How neural language models use context. find
  24. Omar Khattab and Matei Zaharia. 2020. Colbert: Efficient and effective passage search via contextualized late interaction over bert. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. ACM, Xi’an, 39–48.
  25. Bevan Koopman and Guido Zuccon. 2023. Dr ChatGPT tell me what I want to hear: How different prompts impact health answer correctness. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computati… find
  26. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural…
  27. Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019. Latent Retrieval for Weakly Supervised Open Domain Question Answering. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Anna Kor…
  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–…
  29. Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee. 2023. Textbooks are all you need ii: phi-1.5 technical report.
  30. Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023. Lost in the middle: How language models use long contexts.
  31. Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022. Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), S…
  32. C Manning, P Raghavan, and H Schutze. 2008. Term weighting, and the vector space model. Cambridge University Press Cambridge, Cambridge. 109–133 pages. find
  33. Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. 2023. Augmented language models: a survey.
  34. Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020. AmbigQA: Answering Ambiguous Open-domain Questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association fo… find
  35. Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay. 2023. The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only. arXiv:2306.01116 [cs.CL] find
  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 ↗
  37. Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018. Improving language understanding by generative pre-training. find
  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. find
  39. Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023. In-context retrieval-augmented language models.
  40. Stephen Robertson, Hugo Zaragoza, et al. 2009. The probabilistic relevance framework: BM25 and beyond. Foundations and Trends® in Information Retrieval 3, 4 (2009), 333–389. find
  41. Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, and Jason Weston. 2021. Recipes for Building an Open-Domain Chatbot. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational…
  42. Gerard Salton and Michael J. McGill. 1983. Introduction to modern information retrieval. McGraw-Hill (1983). find
  43. Andrea Santilli and Emanuele Rodolà. 2023. Camoscio: an Italian Instruction-tuned LLaMA. arXiv:2307.16456 [cs.CL] find
  44. Artsiom Sauchuk, James Thorne, Alon Halevy, Nicola Tonellotto, and Fabrizio Silvestri. 2022. On the Role of Relevance in Natural Language Processing Tasks. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, Madrid, 1785–1789. find
  45. Noam Shazeer. 2019. Fast Transformer Decoding: One Write-Head is All You Need. arXiv:1911.02150 [cs.NE] find
  46. Simeng Sun, Kalpesh Krishna, Andrew Mattarella-Micke, and Mohit Iyyer. 2021. Do long-range language models actually use long-range context?
  47. MosaicML NLP Team et al. 2023. Introducing mpt-7b: A new standard for open-source, ly usable llms. find
  48. Gabriele Tolomei, Cesare Campagnano, Fabrizio Silvestri, and Giovanni Trappolini. 2023. Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models. In 5th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2023, Atla… — [doi:10.1109/cogmi58952.2023.00027](https://doi.org/10.1109/cogmi58952.2023.00027) find
  49. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models.
  50. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023. Llama 2: Open foundation and fine-tuned chat models.
  51. Giovanni Trappolini, Andrea Santilli, Emanuele Rodolà, Alon Y. Halevy, and Fabrizio Silvestri. 2023. Multimodal Neural Databases. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, Hsin-Hsi… — [doi:10.1145/3539618.3591930](https://doi.org/10.1145/3539618.3591930) find
  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. Gar…
  53. Michael Völske, Martin Potthast, Shahbaz Syed, and Benno Stein. 2017. TL;DR: Mining Reddit to Learn Automatic Summarization. In Proceedings of the Workshop on New Frontiers in Summarization, Lu Wang, Jackie Chi Kit Cheung, Giuseppe Carenini, and Fei Liu (Eds.). Association for Computational Linguist…
  54. Shuai Wang, Liang Ding, Li Shen, Yong Luo, Bo Du, and Dacheng Tao. 2024. OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models. arXiv preprint arXiv:2401.06628 (2024). find
  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.
  56. Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, Yitao Liu, Yiheng Xu, Shuyan Zhou, Silvio Savarese, Caiming Xiong, Victor Zhong, and Tao Yu. 2024. OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in…
  57. Andrew Yates, Rodrigo Nogueira, and Jimmy Lin. 2021. Pretrained Transformers for Text Ranking: BERT and Beyond. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials, Greg Kondrak, Kalina Bontcheva… find
  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. find
  59. Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, and Shaoping Ma. 2021. Optimizing Dense Retrieval Model Training with Hard Negatives. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21). New York, NY, USA, 1503–1… find
  60. Guido Zuccon, Bevan Koopman, and Razia Shaik. 2023. ChatGPT Hallucinates when Attributing Answers. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (Beijing, China) (SIGIR-AP ’23). Association for Computin… — [doi:10.1145/3624918.3625329](https://doi.org/10.1145/3624918.3625329) find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/cuconasu-2024-power-noise-redefining-retrieval-rag/