---
title: "MASS-RAG — paper by Xiao et al. (2026) — summary and ideas"
description: "Because the design is modular, it can be added to existing retrieval pipelines. 76.7 accuracy on TriviaQA. 42 references."
authors: ["Xingchen Xiao", "Heyan Huang", "Runheng Liu", "Jincheng Xie"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.480/"
doi: "10.18653/v1/2026.findings-acl.480"
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/xiao-2026-mass-rag-multi-agent-synthesis/"
---
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# MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation

**Authors:** Xingchen Xiao, [Heyan Huang](https://md.research.elamur.ai/free/en/authors/heyan-huang/), Runheng Liu and Jincheng Xie · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **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/2026.findings-acl.480/) · [PDF](https://aclanthology.org/2026.findings-acl.480.pdf) · [DOI 10.18653/v1/2026.findings-acl.480](https://doi.org/10.18653/v1/2026.findings-acl.480) · [OpenAlex](https://openalex.org/W7166844133)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Retrieval-augmented generation often fails when the retrieved passages are noisy, partial or uneven in quality. In that case a single generation pass, or the single judge agent used for filtering in earlier multi-agent RAG work, can miss evidence that only appears from a different angle. MASS-RAG addresses this without any training. It sends the query and the retrieved documents to three LLM filter agents, each with its own role:
- a Summarizer that condenses the documents,
- an Extractor that copies supporting spans verbatim,
- a Reasoner that makes implicit, cross-document inferences explicit.

An optional Answer Agent turns each filtered view into a candidate answer. A Synthesis Agent then reconciles either these candidates or the filtered views directly into one final prediction. On four benchmarks, the authors report consistent gains over strong RAG baselines, especially when relevant facts are spread across several documents. Their analysis shows that the agents capture different, complementary evidence, which supports using several perspectives instead of one filter. Because the design is modular, it can be added to existing retrieval pipelines.

## Key points
- The pipeline has three stages: evidence distillation (Summarizer, Extractor, Reasoner), optional candidate answer generation (Answer Agent), and final synthesis (Synthesis Agent).
- The Answer Agent is enabled depending on the task. It helps most for factoid QA, adds little for multiple-choice tasks with A/B/C/D labels, and gives limited benefit for long-form QA.
- Experiments use Llama3 8B and Mistral 7B backbones with the top-10 retrieved documents. Evaluation includes TriviaQA and PopQA, and an Evidence Coverage Rate (ECR) metric is computed on a "Uniquely Attributable Subset".
- With Llama3 8B, the Synthesis Agent reaches 76.7 accuracy on TriviaQA, above the Summarizer (73.7), Reasoner (72.8) and Extractor (68.1). On PopQA it scores 64.2, below the Summarizer's 65.7.
- With Mistral 7B, synthesis does not beat the best individual filter on either dataset (73.6 vs 73.8 on TriviaQA; 60.9 vs 62.4 on PopQA). The authors conclude that stronger backbones are better at consolidating filtered evidence toward its upper bound.

*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

- [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 415 works · 94,028 citations
- [Beijing Institute of Technology](https://md.research.elamur.ai/free/en/universities/beijing-institute-of-technology/) — University, Beijing, China · 43 works · 3,652 citations

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## Cite

Cite as: Xingchen Xiao, Heyan Huang, Runheng Liu and Jincheng Xie. 2026. MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{xiao2026mass,
  title = {{MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation}},
  author = {Xingchen Xiao, Heyan Huang, Runheng Liu and Jincheng Xie},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.480},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.480}
}
```

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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/40/)

## References

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

1. 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/). Preprint, arXiv:2310.11511.
2. Akari Asai, Zexuan Zhong, Danqi Chen, Pang Wei Koh, Luke Zettlemoyer, Hannaneh Hajishirzi, and Wen tau Yih. 2024. [Reliable, adaptable, and attributable language models with retrieval](https://md.research.elamur.ai/free/en/papers/asai-2024-reliable-adaptable-attributable-language-models/). Preprint, arXiv:2403.03187.
3. Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha, Roberto de M. Estevão Filho, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, Sara Malvar, Leonardo O. Nunes, Rafael Padilha, Morris Sharp, Bruno Silva, Swati Sharma, Vijay Aski, and Ranveer Chandra. 2024. Rag vs fine-tu… — [arXiv:2401.08406](https://arxiv.org/abs/2401.08406) [find](https://scholar.google.com/scholar?q=Angels+Balaguer%2C+Vinamra+Benara%2C+Renato+Luiz+de+Freitas+Cunha%2C+Roberto+de+M%2E+Estev%C3%A3o+Filho%2C+Todd+Hendry%2C+Daniel+Holstein%2C+Jennifer+Marsman%2C+Nick+Mecklenburg%2C+Sara+Malvar%2C+Leonardo+O%2E+Nunes%2C+Rafael+Pad)
4. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cas… [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+van+den+Driessche%2C+Jean%2DBaptiste+Lespiau%2C+Bogdan+Damoc%2C+Aidan+Clark%2C+Diego+de+Las+Casas%2C+Aureli)
5. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter,… — [Language Models are Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
6. Chi-Min Chan, Chunpu Xu, Ruibin Yuan, Hongyin Luo, Wei Xue, Yike Guo, and Jie Fu. 2024. [Rq-rag: Learning to refine queries for retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/chan-2024-rq-rag-learning-refine-queries/). Preprint, arXiv:2404.00610.
7. Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, Menghai Pan, Chin-Chia Michael Yeh, Guanchu Wang, Mingzhi Hu, Zhichao Xu, Yan Zheng, Mahashweta Das, and Na Zou. 2024. [Main-rag: Multi-agent filtering retrieval-augmented generation](https://md.research.elamur.ai/free/en/papers/chang-2025-main-rag-multi-agent-filtering/). Preprint, arXiv:2501.00332.
8. Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. [Reading wikipedia to answer open-domain questions](https://md.research.elamur.ai/free/en/papers/chen-2017-reading-wikipedia-answer-open-domain/). Preprint, arXiv:1704.00051. 9873 9
9. Yiqun Chen, Lingyong Yan, Weiwei Sun, Xinyu Ma, Yi Zhang, Shuaiqiang Wang, Dawei Yin, Yiming Yang, and Jiaxin Mao. 2025. Improving retrieval-augmented generation through multi-agent reinforcement learning. Preprint, arXiv:2501.15228. — [arXiv:2501.15228](https://arxiv.org/abs/2501.15228) [find](https://scholar.google.com/scholar?q=Yiqun+Chen%2C+Lingyong+Yan%2C+Weiwei+Sun%2C+Xinyu+Ma%2C+Yi+Zhang%2C+Shuaiqiang+Wang%2C+Dawei+Yin%2C+Yiming+Yang%2C+and+Jiaxin+Mao%2E+2025%2E+Improving+retrieval%2Daugmented+generation+through+multi%2Dagent+reinforcement+lear)
10. Sukmin Cho, Jeongyeon Seo, Soyeong Jeong, and Jong C. Park. 2023. Improving zero-shot reader by reducing distractions from irrelevant documents in open-domain question answering. Preprint, arXiv:2310.17490. [find](https://scholar.google.com/scholar?q=Sukmin+Cho%2C+Jeongyeon+Seo%2C+Soyeong+Jeong%2C+and+Jong+C%2E+Park%2E+2023%2E+Improving+zero%2Dshot+reader+by+reducing+distractions+from+irrelevant+documents+in+open%2Ddomain+question+answering%2E+Preprint%2C+arXiv%3A2310%2E)
11. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018. [Think you have solved question answering? try arc, the ai2 reasoning challenge](https://md.research.elamur.ai/free/en/papers/clark-2018-think-have-solved-question-answering/). Preprint, arXiv:1803.05457.
12. Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen. 2023. [Enabling large language models to generate text with citations](https://md.research.elamur.ai/free/en/papers/gao-2023-enabling-large-language-models-generate/). Preprint, arXiv:2305.14627.
13. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Qianyu Guo, Meng Wang, and Haofen Wang. 2024. [Retrieval-augmented generation for large language models: A survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/). Preprint, arXiv:2312.10997.
14. Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Rajaram 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/). Preprint, arXiv:2207.06300.
15. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, and 542 o… — [The Llama 3 Herd of Models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
16. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2023. [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/). Preprint, arXiv:2311.05232.
17. Taeho Hwang, Soyeong Jeong, Sukmin Cho, SeungYoon Han, and Jong C. Park. 2024. Dslr: Document refinement with sentence-level re-ranking and reconstruction to enhance retrieval-augmented generation. Preprint, arXiv:2407.03627. [find](https://scholar.google.com/scholar?q=Taeho+Hwang%2C+Soyeong+Jeong%2C+Sukmin+Cho%2C+SeungYoon+Han%2C+and+Jong+C%2E+Park%2E+2024%2E+Dslr%3A+Document+refinement+with+sentence%2Dlevel+re%2Dranking+and+reconstruction+to+enhance+retrieval%2Daugmented+generation%2E+Pr)
18. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2022a. [Unsupervised dense information retrieval with contrastive learning](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/). Preprint, arXiv:2112.09118.
19. Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022b. [Atlas: Few-shot learning with retrieval augmented language models](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/). Preprint, arXiv:2208.03299.
20. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix,… [find](https://scholar.google.com/scholar?q=Albert+Q%2E+Jiang%2C+Alexandre+Sablayrolles%2C+Arthur+Mensch%2C+Chris+Bamford%2C+Devendra+Singh+Chaplot%2C+Diego+de+las+Casas%2C+Florian+Bressand%2C+Gianna+Lengyel%2C+Guillaume+Lample%2C+Lucile+Saulnier%2C+L%C3%A9lio+Renard+Lav)
21. 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/). Preprint, arXiv:1705.03551.
22. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2021. [Retrieval-augmented generation for knowledge-intensive nlp tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). Preprint, arXiv:2005.11401.
23. Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, Luke Zettlemoyer, and Scott Yih. 2023. [Ra-dit: Retrieval-augmented dual instruction tuning](https://md.research.elamur.ai/free/en/papers/lin-2023-ra-dit-retrieval-augmented-dual/). Preprint, arXiv:2310.01352.
24. 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](https://md.research.elamur.ai/free/en/papers/liu-2023-lost-middle-language-models-use/). Preprint, arXiv:2307.03172.
25. Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023. Query rewriting for retrieval-augmented large language models. Preprint, arXiv:2305.14283. — [arXiv:2305.14283](https://arxiv.org/abs/2305.14283) [find](https://scholar.google.com/scholar?q=Xinbei+Ma%2C+Yeyun+Gong%2C+Pengcheng+He%2C+Hai+Zhao%2C+and+Nan+Duan%2E+2023%2E+Query+rewriting+for+retrieval%2Daugmented+large+language+models%2E+Preprint%2C+arXiv%3A2305%2E14283%2E+%E2%80%94+%5BarXiv%3A2305%2E14283%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs)
26. 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. Preprint, arXiv:2212.10511. [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)
27. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2023. Locating and editing factual associations in gpt. Preprint, arXiv:2202.05262. [find](https://scholar.google.com/scholar?q=Kevin+Meng%2C+David+Bau%2C+Alex+Andonian%2C+and+Yonatan+Belinkov%2E+2023%2E+Locating+and+editing+factual+associations+in+gpt%2E+Preprint%2C+arXiv%3A2202%2E05262%2E)
28. Thang Nguyen, Peter Chin, and Yu-Wing Tai. 2025. [Ma-rag: Multi-agent retrieval-augmented generation via collaborative chain-of-thought reasoning](https://md.research.elamur.ai/free/en/papers/nguyen-2025-ma-rag-multi-agent-retrieval/). Preprint, arXiv:2505.20096.
29. OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mohammad Bavarian, Jeff Belgum, and 262 others.… — [GPT-4 Technical Report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
30. Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023. [In-context retrieval-augmented language models](https://md.research.elamur.ai/free/en/papers/ram-2023-context-retrieval-augmented-language-models/). Preprint, arXiv:2302.00083. 9874 10
31. Qian Ruan, Ilia Kuznetsov, and Iryna Gurevych. 2024. Are large language models good classifiers? a study on edit intent classification in scientific document revisions. Preprint, arXiv:2410.02028. [find](https://scholar.google.com/scholar?q=Qian+Ruan%2C+Ilia+Kuznetsov%2C+and+Iryna+Gurevych%2E+2024%2E+Are+large+language+models+good+classifiers%3F+a+study+on+edit+intent+classification+in+scientific+document+revisions%2E+Preprint%2C+arXiv%3A2410%2E02028%2E)
32. Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen tau Yih. 2023. [Replug: Retrieval-augmented black-box language models](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/). Preprint, arXiv:2301.12652.
33. Haoyan Yang, Zhitao Li, Yong Zhang, Jianzong Wang, Ning Cheng, Ming Li, and Jing Xiao. 2023. Prca: Fitting black-box large language models for retrieval question answering via pluggable reward-driven contextual adapter. Preprint, arXiv:2310.18347. [find](https://scholar.google.com/scholar?q=Haoyan+Yang%2C+Zhitao+Li%2C+Yong+Zhang%2C+Jianzong+Wang%2C+Ning+Cheng%2C+Ming+Li%2C+and+Jing+Xiao%2E+2023%2E+Prca%3A+Fitting+black%2Dbox+large+language+models+for+retrieval+question+answering+via+pluggable+reward%2Ddriven+)
34. Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang. 2023. [Asqa: Factoid questions meet long-form answers](https://md.research.elamur.ai/free/en/papers/stelmakh-2022-asqa-factoid-questions-meet-long/). Preprint, arXiv:2204.06092.
35. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, and 49 others… — [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
36. Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, and Bryan Catanzaro. 2023a. Instructretro: Instruction tuning post retrieval-augmented pretraining. Preprint, arXiv:2310.07713. [find](https://scholar.google.com/scholar?q=Boxin+Wang%2C+Wei+Ping%2C+Lawrence+McAfee%2C+Peng+Xu%2C+Bo+Li%2C+Mohammad+Shoeybi%2C+and+Bryan+Catanzaro%2E+2023a%2E+Instructretro%3A+Instruction+tuning+post+retrieval%2Daugmented+pretraining%2E+Preprint%2C+arXiv%3A2310%2E07713%2E)
37. 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/). Preprint, arXiv:2311.08377.
38. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022. [Emergent abilities of large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/). Preprint, arXiv:22…
39. 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/). Preprint, arXiv:2310.04408.
40. Zhipeng Xu, Zhenghao Liu, Yibin Liu, Chenyan Xiong, Yukun Yan, Shuo Wang, Shi Yu, Zhiyuan Liu, and Ge Yu. 2024a. Activerag: Revealing the treasures of knowledge via active learning. Preprint, arXiv:2402.13547. — [arXiv:2402.13547](https://arxiv.org/abs/2402.13547) [find](https://scholar.google.com/scholar?q=Zhipeng+Xu%2C+Zhenghao+Liu%2C+Yibin+Liu%2C+Chenyan+Xiong%2C+Yukun+Yan%2C+Shuo+Wang%2C+Shi+Yu%2C+Zhiyuan+Liu%2C+and+Ge+Yu%2E+2024a%2E+Activerag%3A+Revealing+the+treasures+of+knowledge+via+active+learning%2E+Preprint%2C+arXiv%3A24)
41. Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli. 2024b. [Hallucination is inevitable: An innate limitation of large language models](https://md.research.elamur.ai/free/en/papers/xu-2024-hallucination-inevitable-innate-limitation-large/). Preprint, arXiv:2401.11817.
42. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, and 41 others. 2025. [Qwen3 technical report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). Preprint, arXiv:2505.09388. 9875 1…

*Updated 2 November 2026*