---
title: "Astute RAG — paper by Wang et al. (2025) — summary and ideas"
description: "Building on this, they propose Astute RAG, a training-free method for black-box LLMs, structured as… 47 references, cited by 35 papers here."
authors: ["Fei Wang", "Xingchen Wan", "Ruoxi Sun", "Jiefeng Chen", "Sercan O Arik"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.1476/"
doi: "10.18653/v1/2025.acl-long.1476"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2025-astute-rag-overcoming-imperfect-retrieval/"
---
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# Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models

**Authors:** [Fei Wang](https://md.research.elamur.ai/free/en/authors/fei-wang/), [Xingchen Wan](https://md.research.elamur.ai/free/en/authors/xingchen-wan/), [Ruoxi Sun](https://md.research.elamur.ai/free/en/authors/ruoxi-sun/), [Jiefeng Chen](https://md.research.elamur.ai/free/en/authors/jiefeng-chen/) and [Sercan O Arik](https://md.research.elamur.ai/free/en/authors/sercan-o-arik/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.1476/) · [PDF](https://aclanthology.org/2025.acl-long.1476.pdf) · [DOI 10.18653/v1/2025.acl-long.1476](https://doi.org/10.18653/v1/2025.acl-long.1476) · [OpenAlex](https://openalex.org/W4412889733)

**Citations:** 24 citations (OpenAlex, 2026-10-14) · [35](https://md.research.elamur.ai/free/en/papers/wang-2025-astute-rag-overcoming-imperfect-retrieval/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Retrieval-augmented generation often receives passages that are irrelevant, misleading or even adversarial. These passages can clash with what the LLM already "knows" and degrade its answers. The authors first measure this problem under realistic conditions. They draw 1K questions from NQ, TriviaQA, BioASQ and PopQA and retrieve 10 passages per question from the Web via Google Search. They find that low-precision retrieval is common, and that knowledge conflicts become more frequent as precision drops. Internal and external knowledge frequently fix each other's mistakes.

Building on this, they propose Astute RAG, a training-free method for black-box LLMs, structured as three stages:

1. **Adaptive generation:** the model writes its own passages from parametric memory, generating more or fewer as needed.
2. **Source-aware consolidation:** these are merged with the retrieved texts while tracking where each piece came from, grouping consistent facts, separating conflicting ones and dropping irrelevant material.
3. **Answer selection:** the final answer is chosen by comparing candidates by reliability.

Experiments with Gemini, Claude and Mistral show gains over earlier robustness-oriented RAG methods. Astute RAG is the only approach that does no worse than a retrieval-free LLM when every retrieved passage is unhelpful. This matters for building trustworthy RAG systems on noisy corpora.

## Key points
- About 70% of passages returned by Google Search did not directly contain the correct answer. With Claude 3.5 Sonnet, 19.2% of instances showed a conflict between answers with and without RAG.
- Conflict rates peak at very low retrieval precision (around 10%) and generally decline as precision rises, across all tested LLMs.
- On instances where RAG and no-RAG answers disagree, Astute RAG picks the correct one in roughly 80% of cases. It also helps on some instances where neither source alone is right.
- Compared with the plain RAG baseline, it costs under 5% more tokens and API calls while improving accuracy by over 11%. Additional consolidation iterations bring further but diminishing gains.
- An LLM-as-a-judge evaluation rated the intermediate steps as 98.2% accurate for knowledge consolidation and 95.0% for confidence assignment. Removing source labels lowered performance, especially on NQ, PopQA and BioASQ.

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

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## Cite

Cite as: Fei Wang, Xingchen Wan, Ruoxi Sun, Jiefeng Chen and Sercan O Arik. 2025. Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2025astute,
  title = {{Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models}},
  author = {Fei Wang, Xingchen Wan, Ruoxi Sun, Jiefeng Chen and Sercan O Arik},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.1476},
  url = {https://doi.org/10.18653/v1/2025.acl-long.1476}
}
```

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## References

47 references, 27 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Marco Alessio, Guglielmo Faggioli, Nicola Ferro, Franco Maria Nardini, Raffaele Perego, et al. 2024. Improving rag systems via sentence clustering and reordering. In RAG@ SIGIR 2024 workshop: The Information Retrieval’s Role in RAG Systems, ACM, pages 1–10. [find](https://scholar.google.com/scholar?q=Marco+Alessio%2C+Guglielmo+Faggioli%2C+Nicola+Ferro%2C+Franco+Maria+Nardini%2C+Raffaele+Perego%2C+et+al%2E+2024%2E+Improving+rag+systems+via+sentence+clustering+and+reordering%2E+In+RAG%40+SIGIR+2024+workshop%3A+The+Info)
2. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023. [Self-rag: Learning to retrieve, generate, and critique through self-reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/). In The Twelfth International Conference on Learning Representations.
3. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022. [Constitutional ai: Harmlessness from ai feedback](https://md.research.elamur.ai/free/en/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/). arXiv preprint arXiv:2212.08073.
4. 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](https://scholar.google.com/scholar?q=Sebastian+Borgeaud%2C+Arthur+Mensch%2C+Jordan+Hoffmann%2C+Trevor+Cai%2C+Eliza+Rutherford%2C+Katie+Millican%2C+George+Bm+Van+Den+Driessche%2C+Jean%2DBaptiste+Lespiau%2C+Bogdan+Damoc%2C+Aidan+Clark%2C+et+al%2E+2022%2E+Improving+)
5. Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024a. [Benchmarking large language models in retrieval-augmented generation](https://md.research.elamur.ai/free/en/papers/chen-2023-benchmarking-large-language-models-retrieval/). In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 17754–17762.
6. Xinyun Chen, Renat Aksitov, Uri Alon, Jie Ren, Kefan Xiao, Pengcheng Yin, Sushant Prakash, Charles Sutton, Xuezhi Wang, and Denny Zhou. 2024b. Universal self-consistency for large language models. In ICML 2024 Workshop on In-Context Learning. [find](https://scholar.google.com/scholar?q=Xinyun+Chen%2C+Renat+Aksitov%2C+Uri+Alon%2C+Jie+Ren%2C+Kefan+Xiao%2C+Pengcheng+Yin%2C+Sushant+Prakash%2C+Charles+Sutton%2C+Xuezhi+Wang%2C+and+Denny+Zhou%2E+2024b%2E+Universal+self%2Dconsistency+for+large+language+models%2E+In+)
7. 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](https://md.research.elamur.ai/free/en/papers/cuconasu-2024-power-noise-redefining-retrieval-rag/). In Proceedings of the 47th International ACM SIGIR Conference on Research and Developm…
8. Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, and Jun Xu. 2024. Unifying bias and unfairness in information retrieval: A survey of challenges and opportunities with large language models. arXiv preprint arXiv:2404.11457. — [arXiv:2404.11457](https://arxiv.org/abs/2404.11457) [find](https://scholar.google.com/scholar?q=Sunhao+Dai%2C+Chen+Xu%2C+Shicheng+Xu%2C+Liang+Pang%2C+Zhenhua+Dong%2C+and+Jun+Xu%2E+2024%2E+Unifying+bias+and+unfairness+in+information+retrieval%3A+A+survey+of+challenges+and+opportunities+with+large+language+models)
9. Feiteng Fang, Yuelin Bai, Shiwen Ni, Min Yang, Xiaojun Chen, and Ruifeng Xu. 2024. [Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training](https://md.research.elamur.ai/free/en/papers/fang-2024-enhancing-noise-robustness-retrieval-augmented/). arXiv preprint arXiv:2405.20978.
10. Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, and Alfio Gliozzo. 2022. [Re2g: Retrieve, rerank, generate](https://md.research.elamur.ai/free/en/papers/glass-2022-re2g-retrieve-rerank-generate/). In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages…
11. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020. [Retrieval augmented language model pre-training](https://md.research.elamur.ai/free/en/papers/guu-2020-realm-retrieval-augmented-language-model/). In International conference on machine learning, pages 3929–3938. PMLR.
12. Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong C Park. 2024. [Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity](https://md.research.elamur.ai/free/en/papers/jeong-2024-adaptive-rag-learning-adapt-retrieval/). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics:…
13. Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. [Active retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/jiang-2023-active-retrieval-augmented-generation/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7969–7992.
14. Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Li Qiuxia, and Jun Zhao. 2024. [Tug-of-war between knowledge: Exploring and resolving knowledge conflicts in retrieval-augmented language models](https://md.research.elamur.ai/free/en/papers/jin-2024-tug-war-between-knowledge-exploring/). In Proceedings of the 2024 Joint International Conference on Computational Lingui…
15. Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017. [Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1601–1611.
16. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019. [Natural questions: a benchmark for question answering research](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/). Transactions of the Association for Computational Linguistic…
17. 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](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). Advances in Neural Information Processing Systems, 33:9459–9474.
18. Shayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh. 2021. [Entity-based knowledge conflicts in question answering](https://md.research.elamur.ai/free/en/papers/longpre-2021-entity-based-knowledge-conflicts-question/). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
19. Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023. [Query rewriting in retrieval-augmented large language models](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5303–5315.
20. Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. When not to trust language models: Investigating effectiveness of parametric and non-parametric memories. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (V… [find](https://scholar.google.com/scholar?q=Alex+Mallen%2C+Akari+Asai%2C+Victor+Zhong%2C+Rajarshi+Das%2C+Daniel+Khashabi%2C+and+Hannaneh+Hajishirzi%2E+2023%2E+When+not+to+trust+language+models%3A+Investigating+effectiveness+of+parametric+and+non%2Dparametric+mem)
21. Ruotong Pan, Boxi Cao, Hongyu Lin, Xianpei Han, Jia Zheng, Sirui Wang, Xunliang Cai, and Le Sun. 2024. Not all contexts are equal: Teaching llms credibility-aware generation. arXiv preprint arXiv:2404.06809. [find](https://scholar.google.com/scholar?q=Ruotong+Pan%2C+Boxi+Cao%2C+Hongyu+Lin%2C+Xianpei+Han%2C+Jia+Zheng%2C+Sirui+Wang%2C+Xunliang+Cai%2C+and+Le+Sun%2E+2024%2E+Not+all+contexts+are+equal%3A+Teaching+llms+credibility%2Daware+generation%2E+arXiv+preprint+arXiv%3A2404)
22. Quang Hieu Pham, Hoang Ngo, Anh Tuan Luu, and Dat Quoc Nguyen. 2024. Who’s who: Large language models meet knowledge conflicts in practice. arXiv preprint arXiv:2410.15737. [find](https://scholar.google.com/scholar?q=Quang+Hieu+Pham%2C+Hoang+Ngo%2C+Anh+Tuan+Luu%2C+and+Dat+Quoc+Nguyen%2E+2024%2E+Who%E2%80%99s+who%3A+Large+language+models+meet+knowledge+conflicts+in+practice%2E+arXiv+preprint+arXiv%3A2410%2E15737%2E)
23. Chong Xiang, Tong Wu, Zexuan Zhong, David Wagner, Danqi Chen, and Prateek Mittal. 2024. Certifiably robust rag against retrieval corruption. arXiv preprint arXiv:2405.15556. [find](https://scholar.google.com/scholar?q=Chong+Xiang%2C+Tong+Wu%2C+Zexuan+Zhong%2C+David+Wagner%2C+Danqi+Chen%2C+and+Prateek+Mittal%2E+2024%2E+Certifiably+robust+rag+against+retrieval+corruption%2E+arXiv+preprint+arXiv%3A2405%2E15556%2E)
24. Rulin Shao, Jacqueline He, Akari Asai, Weijia Shi, Tim Dettmers, Sewon Min, Luke Zettlemoyer, and Pang Wei Koh. 2024. Scaling retrieval-based language models with a trillion-token datastore. arXiv preprint arXiv:2407.12854. [find](https://scholar.google.com/scholar?q=Rulin+Shao%2C+Jacqueline+He%2C+Akari+Asai%2C+Weijia+Shi%2C+Tim+Dettmers%2C+Sewon+Min%2C+Luke+Zettlemoyer%2C+and+Pang+Wei+Koh%2E+2024%2E+Scaling+retrieval%2Dbased+language+models+with+a+trillion%2Dtoken+datastore%2E+arXiv+pre)
25. Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2024. [Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts](https://md.research.elamur.ai/free/en/papers/xie-2023-adaptive-chameleon-stubborn-sloth-revealing/). In The Twelfth International Conference on Learning Representations.
26. Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang. 2022. [Asqa: Factoid questions meet long-form answers](https://md.research.elamur.ai/free/en/papers/stelmakh-2022-asqa-factoid-questions-meet-long/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8273–8288.
27. Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2023. [Recomp: Improving retrieval-augmented lms with compression and selective augmentation](https://md.research.elamur.ai/free/en/papers/xu-2023-recomp-improving-retrieval-augmented-lms/). arXiv preprint arXiv:2310.04408.
28. Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han-yu Wang, Haisu Liu, Quan Shi, Zachary S Siegel, Michael Tang, et al. 2024. [Bright: A realistic and challenging benchmark for reasoning-intensive retrieval](https://md.research.elamur.ai/free/en/papers/su-2024-bright-realistic-challenging-benchmark-reasoning/). arXiv preprint arXiv:2407.12883.
29. Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, and Bryan Catanzaro. 2024. [Retrieval meets long context large language models](https://md.research.elamur.ai/free/en/papers/xu-2023-retrieval-meets-long-context-large/). In The Twelfth International Conference on Learning Representations.
30. Hexiang Tan, Fei Sun, Wanli Yang, Yuanzhuo Wang, Qi Cao, and Xueqi Cheng. 2024. Blinded by generated contexts: How language models merge generated and retrieved contexts for open-domain qa? arXiv preprint arXiv:2401.11911. — [arXiv:2401.11911](https://arxiv.org/abs/2401.11911) [find](https://scholar.google.com/scholar?q=Hexiang+Tan%2C+Fei+Sun%2C+Wanli+Yang%2C+Yuanzhuo+Wang%2C+Qi+Cao%2C+and+Xueqi+Cheng%2E+2024%2E+Blinded+by+generated+contexts%3A+How+language+models+merge+generated+and+retrieved+contexts+for+open%2Ddomain+qa%3F+arXiv+prep)
31. Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling. 2024. [Corrective retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/yan-2024-corrective-retrieval-augmented-generation/). arXiv preprint arXiv:2401.15884.
32. Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2024. Beir: A heterogeneous benchmark for zero-shot evaluation of information retrieval models. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). [find](https://scholar.google.com/scholar?q=Nandan+Thakur%2C+Nils+Reimers%2C+Andreas+R%C3%BCckl%C3%A9%2C+Abhishek+Srivastava%2C+and+Iryna+Gurevych%2E+2024%2E+Beir%3A+A+heterogeneous+benchmark+for+zero%2Dshot+evaluation+of+information+retrieval+models%2E+In+Thirty%2Dfifth+Co)
33. George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopoulos, et al. 2015. An overview of the bioasq large-scale biomedical semantic indexing and question answeri… [find](https://scholar.google.com/scholar?q=George+Tsatsaronis%2C+Georgios+Balikas%2C+Prodromos+Malakasiotis%2C+Ioannis+Partalas%2C+Matthias+Zschunke%2C+Michael+R+Alvers%2C+Dirk+Weissenborn%2C+Anastasia+Krithara%2C+Sergios+Petridis%2C+Dimitris+Polychronopoulos%2C+)
34. Haotian Wang, Xiyuan Du, Weijiang Yu, Qianglong Chen, Kun Zhu, Zheng Chu, Lian Yan, and Yi Guan. 2023a. Apollo’s oracle: Retrieval-augmented reasoning in multi-agent debates. arXiv preprint arXiv:2312.04854. — [arXiv:2312.04854](https://arxiv.org/abs/2312.04854) [find](https://scholar.google.com/scholar?q=Haotian+Wang%2C+Xiyuan+Du%2C+Weijiang+Yu%2C+Qianglong+Chen%2C+Kun+Zhu%2C+Zheng+Chu%2C+Lian+Yan%2C+and+Yi+Guan%2E+2023a%2E+Apollo%E2%80%99s+oracle%3A+Retrieval%2Daugmented+reasoning+in+multi%2Dagent+debates%2E+arXiv+preprint+arXiv%3A2312)
35. Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, and Graham Neubig. 2023b. [Learning to filter context for retrieval-augmented generation](https://md.research.elamur.ai/free/en/papers/wang-2023-learning-filter-context-retrieval-augmented/). arXiv preprint arXiv:2311.08377.
36. Zilong Wang, Zifeng Wang, Long Le, Huaixiu Steven Zheng, Swaroop Mishra, Vincent Perot, Yuwei Zhang, Anush Mattapalli, Ankur Taly, Jingbo Shang, et al. 2024. Speculative rag: Enhancing retrieval augmented generation through drafting. arXiv preprint arXiv:2407.08223. — [arXiv:2407.08223](https://arxiv.org/abs/2407.08223) [find](https://scholar.google.com/scholar?q=Zilong+Wang%2C+Zifeng+Wang%2C+Long+Le%2C+Huaixiu+Steven+Zheng%2C+Swaroop+Mishra%2C+Vincent+Perot%2C+Yuwei+Zhang%2C+Anush+Mattapalli%2C+Ankur+Taly%2C+Jingbo+Shang%2C+et+al%2E+2024%2E+Speculative+rag%3A+Enhancing+retrieval+augme)
37. Zhepei Wei, Wei-Lin Chen, and Yu Meng. 2024. Instructrag: Instructing retrieval-augmented generation with explicit denoising. arXiv preprint arXiv:2406.13629. — [arXiv:2406.13629](https://arxiv.org/abs/2406.13629) [find](https://scholar.google.com/scholar?q=Zhepei+Wei%2C+Wei%2DLin+Chen%2C+and+Yu+Meng%2E+2024%2E+Instructrag%3A+Instructing+retrieval%2Daugmented+generation+with+explicit+denoising%2E+arXiv+preprint+arXiv%3A2406%2E13629%2E+%E2%80%94+%5BarXiv%3A2406%2E13629%5D%28https%3A%2F%2Farxiv%2Eorg%2Fab)
38. Xiao Yang, Kai Sun, Hao Xin, Yushi Sun, Nikita Bhalla, Xiangsen Chen, Sajal Choudhary, Rongze Daniel Gui, Ziran Will Jiang, Ziyu Jiang, et al. 2024. Crag–comprehensive rag benchmark. arXiv preprint arXiv:2406.04744. — [arXiv:2406.04744](https://arxiv.org/abs/2406.04744) [find](https://scholar.google.com/scholar?q=Xiao+Yang%2C+Kai+Sun%2C+Hao+Xin%2C+Yushi+Sun%2C+Nikita+Bhalla%2C+Xiangsen+Chen%2C+Sajal+Choudhary%2C+Rongze+Daniel+Gui%2C+Ziran+Will+Jiang%2C+Ziyu+Jiang%2C+et+al%2E+2024%2E+Crag%E2%80%93comprehensive+rag+benchmark%2E+arXiv+preprint+ar)
39. Chanwoong Yoon, Taewhoo Lee, Hyeon Hwang, Minbyul Jeong, and Jaewoo Kang. 2024. [Compact: Compressing retrieved documents actively for question answering](https://md.research.elamur.ai/free/en/papers/yoon-2024-compact-compressing-retrieved-documents-actively/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 21424–21439.
40. Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. 2024. [Making retrieval-augmented language models robust to irrelevant context](https://md.research.elamur.ai/free/en/papers/yoran-2023-making-retrieval-augmented-language-models/). In The Twelfth International Conference on Learning Representations.
41. Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, and Meng Jiang. 2023a. [Generate rather than retrieve: Large language models are strong context generators](https://md.research.elamur.ai/free/en/papers/yu-2022-generate-rather-than-retrieve-large/). In The Eleventh International Conference on Learning Representations.
42. Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, and Dong Yu. 2023b. [Chain-of-note: Enhancing robustness in retrieval-augmented language models](https://md.research.elamur.ai/free/en/papers/yu-2024-chain-note-enhancing-robustness-retrieval/). arXiv preprint arXiv:2311.09210.
43. Yue Yu, Wei Ping, Zihan Liu, Boxin Wang, Jiaxuan You, Chao Zhang, Mohammad Shoeybi, and Bryan Catanzaro. 2024. Rankrag: Unifying context ranking with retrieval-augmented generation in llms. arXiv preprint arXiv:2407.02485. — [arXiv:2407.02485](https://arxiv.org/abs/2407.02485) [find](https://scholar.google.com/scholar?q=Yue+Yu%2C+Wei+Ping%2C+Zihan+Liu%2C+Boxin+Wang%2C+Jiaxuan+You%2C+Chao+Zhang%2C+Mohammad+Shoeybi%2C+and+Bryan+Catanzaro%2E+2024%2E+Rankrag%3A+Unifying+context+ranking+with+retrieval%2Daugmented+generation+in+llms%2E+arXiv+prep)
44. Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, and Lu Wang. 2023. Merging generated and retrieved knowledge for open-domain qa. In The 2023 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Yunxiang+Zhang%2C+Muhammad+Khalifa%2C+Lajanugen+Logeswaran%2C+Moontae+Lee%2C+Honglak+Lee%2C+and+Lu+Wang%2E+2023%2E+Merging+generated+and+retrieved+knowledge+for+open%2Ddomain+qa%2E+In+The+2023+Conference+on+Empirical+M)
45. Zheng Zhao, Emilio Monti, Jens Lehmann, and Haytham Assem. 2024. Enhancing contextual understanding in large language models through contrastive decoding. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies… [find](https://scholar.google.com/scholar?q=Zheng+Zhao%2C+Emilio+Monti%2C+Jens+Lehmann%2C+and+Haytham+Assem%2E+2024%2E+Enhancing+contextual+understanding+in+large+language+models+through+contrastive+decoding%2E+In+Proceedings+of+the+2024+Conference+of+the+)
46. Wei Zou, Runpeng Geng, Binghui Wang, and Jinyuan Jia. 2024. [Poisonedrag: Knowledge poisoning attacks to retrieval-augmented generation of large language models](https://md.research.elamur.ai/free/en/papers/zou-2024-poisonedrag-knowledge-corruption-attacks-retrieval/). arXiv preprint arXiv:2402.07867. m̂=1 m̂=2
47. NQ TriviaQA BioASQ PopQA Overall m 52.20 52.20 84.10 85.16 60.14 60.84 44.38 43.26 61.71 62.00 0.69 1.24 *Table 2: Performance and averge number of generaed passages using different m̂.* *Figure 11: Efficiency in terms of API calls.* Model NQ TriviaQA BioASQ PopQA Overall [find](https://scholar.google.com/scholar?q=NQ+TriviaQA+BioASQ+PopQA+Overall+m+52%2E20+52%2E20+84%2E10+85%2E16+60%2E14+60%2E84+44%2E38+43%2E26+61%2E71+62%2E00+0%2E69+1%2E24+%2ATable+2%3A+Performance+and+averge+number+of+generaed+passages+using+different+m%CC%82%2E%2A+%2AFigure+11%3A+E)

*Updated 2 November 2026*