Fetching the paper…
Reading the bibliography…
Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs).
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Earlier work this paper cites.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer · 2017
Earlier work this paper cites.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning · 2018
Earlier work this paper cites.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al · 2019
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick S. H. 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 · 2020
Earlier work this paper cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
Earlier work this paper cites.
Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa · 2020
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
Earlier work this paper cites.
DocPrompting: Generating code by retrieving the docs
Shuyan Zhou, Uri Alon, Frank F Xu, Zhengbao Jiang, and Graham Neubig · 2022
Earlier work this paper cites.
ASQA: Factoid questions meet long-form answers
Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Earlier work this paper cites.
Improving hownet-based chinese word sense disambiguation with translations
Xiang Zhang, Bradley Hauer, and Grzegorz Kondrak · 2022
Earlier work this paper cites.
Improving language models by retrieving from trillions of tokens
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
Earlier work this paper cites.
A character-level length-control algorithm for non-autoregressive sentence summarization
Puyuan Liu, Xiang Zhang, and Lili Mou · 2022
Earlier work this paper cites.
Atlas: Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave · 2023
Earlier work this paper cites.
Xiang Zhang, Senyu Li, Bradley Hauer, Ning Shi, and Grzegorz Kondrak · 2023
Earlier work this paper cites.
REPLUG: Retrieval-augmented black-box language models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih · 2023
Earlier work this paper cites.
Self-RAG: Learning to retrieve, generate, and critique through self-reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi · 2023
Earlier work this paper cites.
When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi · 2023
Earlier work this paper cites.
Enabling large language models to generate text with citations
Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen · 2023
Earlier work this paper cites.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
Earlier work this paper cites.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang · 2023
Earlier work this paper cites.
Poisoning retrieval corpora by injecting adversarial passages
Zexuan Zhong, Ziqing Huang, Alexander Wettig, and Danqi Chen · 2023
Earlier work this paper cites.
CacheGen: Fast context loading for language model applications
Yuhan Liu, Hanchen Li, Kuntai Du, Jiayi Yao, Yihua Cheng, Yuyang Huang, Shan Lu, Michael Maire, Henry Hoffmann, Ari Holtzman, et al · 2023
Earlier work this paper cites.
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2023
Earlier work this paper cites.
Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
Earlier work this paper cites.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
Cited alongside, same era.
RealTime QA: What’s the answer right now?
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A Smith, Yejin Choi, Kentaro Inui, et al · 2024
Cited alongside, same era.
CRAG – comprehensive RAG benchmark
Xiao Yang, Kai Sun, Hao Xin, Yushi Sun, Nikita Bhalla, Xiangsen Chen, Sajal Choudhary, Rongze Daniel Gui, Ziran Will Jiang, Ziyu Jiang, Lingkun Kong, Brian Moran, Jiaqi Wang, Yifan Ethan Xu, An Yan, Chenyu Yang, Eting Yuan, Hanwen Zha, Nan Tang, Lei Chen, Nicolas Scheffer, Yue Liu, Nirav Shah, Rakesh Wanga, Anuj Kumar, Wen tau Yih, and Xin Luna Dong · 2024
Cited alongside, same era.
BRIGHT: A realistic and challenging benchmark for reasoning-intensive retrieval
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
Cited alongside, same era.
PoisonedRAG: Knowledge poisoning attacks to retrieval-augmented generation of large language models
Wei Zou, Runpeng Geng, Binghui Wang, and Jinyuan Jia · 2024
Later among the works it cites.
RAGCache: Efficient knowledge caching for retrieval-augmented generation
Chao Jin, Zili Zhang, Xuanlin Jiang, Fangyue Liu, Xin Liu, Xuanzhe Liu, and Xin Jin · 2024
Later among the works it cites.
Cross-modal consistency in multimodal large language models
Xiang Zhang, Senyu Li, Ning Shi, Bradley Hauer, Zijun Wu, Grzegorz Kondrak, Muhammad Abdul-Mageed, and Laks VS Lakshmanan · 2024
Later among the works it cites.
RAPTOR: Recursive abstractive processing for tree-organized retrieval
Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D Manning · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhepei Wei, Wei-Lin Chen, and Yu Meng · 2024
Cited alongside, same era.
RAG vs Fine-tuning: Pipelines, tradeoffs, and a case study on agriculture
Aman Gupta, Anup Shirgaonkar, Angels de Luis Balaguer, Bruno Silva, Daniel Holstein, Dawei Li, Jennifer Marsman, Leonardo O Nunes, Mahsa Rouzbahman, Morris Sharp, et al · 2024
Cited alongside, same era.
RATT: A thought structure for coherent and correct LLM reasoning
Jinghan Zhang, Xiting Wang, Weijieying Ren, Lu Jiang, Dongjie Wang, and Kunpeng Liu · 2024
Cited alongside, same era.
RAFT: Adapting language model to domain specific RAG
Tianjun Zhang, Shishir G Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, and Joseph E Gonzalez · 2024
Cited alongside, same era.
Understanding the dark side of llms’ intrinsic self-correction
Qingjie Zhang, Han Qiu, Di Wang, Haoting Qian, Yiming Li, Tianwei Zhang, and Minlie Huang · 2024
Cited alongside, same era.
Progress or regress? self-improvement reversal in post-training
Ting Wu, Xuefeng Li, and Pengfei Liu · 2024
Cited alongside, same era.
Llm critics help catch llm bugs
Nat McAleese, Rai Michael Pokorny, Juan Felipe Ceron Uribe, Evgenia Nitishinskaya, Maja Trebacz, and Jan Leike · 2024
Cited alongside, same era.
Textgrad: Automatic" differentiation" via text
Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou · 2024
Cited alongside, same era.
Di Zhang, Xiaoshui Huang, Dongzhan Zhou, Yuqiang Li, and Wanli Ouyang · 2024
Later among the works it cites.
V-star: Training verifiers for self-taught reasoners
Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron Courville, Alessandro Sordoni, and Rishabh Agarwal · 2024
Later among the works it cites.
FlashRAG: A modular toolkit for efficient retrieval-augmented generation research
Jiajie Jin, Yutao Zhu, Xinyu Yang, Chenghao Zhang, and Zhicheng Dou · 2024
Later among the works it cites.
Tokenization constraints in llms: A study of symbolic and arithmetic reasoning limits
Xiang Zhang, Juntai Cao, Jiaqi Wei, Yiwei Xu, and Chenyu You · 2025
Closest in time.
Search-r1: Training llms to reason and leverage search engines with reinforcement learning
Bowen Jin, Hansi Zeng, Zhenrui Yue, Dong Wang, Hamed Zamani, and Jiawei Han · 2025
Closest in time.
When more is less: Understanding chain-of-thought length in llms
Yuyang Wu, Yifei Wang, Tianqi Du, Stefanie Jegelka, and Yisen Wang · 2025
Closest in time.
Rare: Retrieval-augmented reasoning modeling
Zhengren Wang, Jiayang Yu, Dongsheng Ma, Zhe Chen, Yu Wang, Zhiyu Li, Feiyu Xiong, Yanfeng Wang, Linpeng Tang, Wentao Zhang, et al · 2025
Closest in time.
Preference leakage: A contamination problem in llm-as-a-judge
Dawei Li, Renliang Sun, Yue Huang, Ming Zhong, Bohan Jiang, Jiawei Han, Xiangliang Zhang, Wei Wang, and Huan Liu · 2025
Closest in time.
Towards reasoning era: A survey of long chain-of-thought for reasoning large language models
Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, and Wangxiang Che · 2025
Closest in time.
Cognitive-aligned document selection for retrieval-augmented generation
Bingyu Wan, Fuxi Zhang, Zhongpeng Qi, Jiayi Ding, Jijun Li, Baoshi Fan, Yijia Zhang, and Jun Zhang · 2025
Closest in time.
Optimizing knowledge integration in retrieval-augmented generation with self-selection
Yan Weng, Fengbin Zhu, Tong Ye, Haoyan Liu, Fuli Feng, and Tat-Seng Chua · 2025
Closest in time.
Multi2: Multi-agent test-time scalable framework for multi-document processing
Juntai Cao, Xiang Zhang, Raymond Li, Chuyuan Li, Chenyu You, Shafiq Joty, and Giuseppe Carenini · 2025
Closest in time.
Postergen: Aesthetic-aware paper-to-poster generation via multi-agent llms
Zhilin Zhang, Xiang Zhang, Jiaqi Wei, Yiwei Xu, and Chenyu You · 2025
Closest in time.
Xiang Zhang, Juntai Cao, Jiaqi Wei, Chenyu You, and Dujian Ding · 2025
Closest in time.
Shuai Zhao, Linchao Zhu, and Yi Yang · 2025
Closest in time.
Critique fine-tuning: Learning to critique is more effective than learning to imitate
Yubo Wang, Xiang Yue, and Wenhu Chen · 2025
Closest in time.
Minirag: Towards extremely simple retrieval-augmented generation
Tianyu Fan, Jingyuan Wang, Xubin Ren, and Chao Huang · 2025
Closest in time.
R1-searcher: Incentivizing the search capability in llms via reinforcement learning
Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen, Zhipeng Chen, Wayne Xin Zhao, Lei Fang, and Ji-Rong Wen · 2025
Closest in time.
Uncovering memorization effect in the presence of spurious correlations
Chenyu You, Haocheng Dai, Yifei Min, Jasjeet S Sekhon, Sarang Joshi, and James S Duncan · 2025
Closest in time.
Rankcot: Refining knowledge for retrieval-augmented generation through ranking chain-of-thoughts
Mingyan Wu, Zhenghao Liu, Yukun Yan, Xinze Li, Shi Yu, Zheni Zeng, Yu Gu, and Ge Yu · 2025
Closest in time.
Zhicheng Lee, Shulin Cao, Jinxin Liu, Jiajie Zhang, Weichuan Liu, Xiaoyin Che, Lei Hou, and Juanzi Li · 2025
Closest in time.
Mm-verify: Enhancing multimodal reasoning with chain-of-thought verification
Linzhuang Sun, Hao Liang, Jingxuan Wei, Bihui Yu, Tianpeng Li, Fan Yang, Zenan Zhou, and Wentao Zhang · 2025
Closest in time.