Fetching the paper…
Reading the bibliography…
Retrieval-augmented generation (RAG) is widely utilized to incorporate external knowledge into large language models, thereby enhancing factuality and reducing hallucinations in question-answering (QA) tasks.
Cumulated gain-based evaluation of ir techniques
Kalervo Järvelin and Jaana Kekäläinen · 2002
Earlier work this paper cites.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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.
Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova · 2019
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.
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.
Distilling knowledge from reader to retriever for question answering
Gautier Izacard and Edouard Grave · 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.
Ambigqa: Answering ambiguous open-domain questions
Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2020
Earlier work this paper cites.
Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder De Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2020
Earlier work this paper cites.
Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave · 2021
Earlier work this paper cites.
End-to-end training of multi-document reader and retriever for open-domain question answering
Devendra Singh, Siva Reddy, Will Hamilton, Chris Dyer, and Dani Yogatama · 2021
Earlier work this paper cites.
Ptde: Personalized training with distilled execution for multi-agent reinforcement learning
Yiqun Chen, Hangyu Mao, Jiaxin Mao, Shiguang Wu, Tianle Zhang, Bin Zhang, Wei Yang, and Hongxing Chang · 2022
Earlier work this paper cites.
Ptde: Personalized training with distillated execution for multi-agent reinforcement learning
Yiqun Chen, Hangyu Mao, Tianle Zhang, Shiguang Wu, Bin Zhang, Jianye Hao, Dong Li, Bin Wang, and Hongxing Chang · 2022
Earlier work this paper cites.
Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang · 2022
Earlier work this paper cites.
Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp
Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Earlier work this paper cites.
Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei · 2022
Cited alongside, same era.
The surprising effectiveness of ppo in cooperative multi-agent games
Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu · 2022
Cited alongside, same era.
Self-rag: Learning to retrieve, generate, and critique through self-reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi · 2023
Cited alongside, same era.
Gptscore: Evaluate as you desire
Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu · 2023
Cited alongside, same era.
Retrieval-augmented generation for large language models: A survey
Rag-ddr: Optimizing retrieval-augmented generation using differentiable data rewards
Xinze Li, Sen Mei, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Hao Chen, Ge Yu, Zhiyuan Liu, et al · 2024
Later among the works it 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 · 2024
Later among the works it cites.
Learning to rank for multiple retrieval-augmented models through iterative utility maximization
Alireza Salemi and Hamed Zamani · 2024
Later among the works it cites.
Towards a search engine for machines: Unified ranking for multiple retrieval-augmented large language models
Alireza Salemi and Hamed Zamani · 2024
Later among the works it cites.
Generate-then-ground in retrieval-augmented generation for multi-hop question answering
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang · 2023
Cited alongside, same era.
Coascore: Chain-of-aspects prompting for nlg evaluation
Peiyuan Gong and Jiaxin Mao · 2023
Cited alongside, same era.
Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu · 2023
Cited alongside, same era.
Active retrieval augmented generation
Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
Cited alongside, same era.
Query rewriting for retrieval-augmented large language models
Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan · 2023
Cited alongside, same era.
Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy
Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen · 2023
Cited alongside, same era.
Is chatgpt good at search? investigating large language models as re-ranking agents
Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren · 2023
Cited alongside, same era.
Making retrieval-augmented language models robust to irrelevant context
Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant · 2023
Cited alongside, same era.
Zhengliang Shi, Weiwei Sun, Shen Gao, Pengjie Ren, Zhumin Chen, and Zhaochun Ren · 2024
Later among the works it cites.
Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, and Yiqun Liu · 2024
Later among the works it cites.
Fei Wang, Xingchen Wan, Ruoxi Sun, Jiefeng Chen, and Sercan Ö Arık · 2024
Later among the works it cites.
C-pack: Packed resources for general chinese embeddings
Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff, Defu Lian, and Jian-Yun Nie · 2024
Later among the works it cites.
Search-in-the-chain: Interactively enhancing large language models with search for knowledge-intensive tasks
Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng, and Tat-Seng Chua · 2024
Later among the works it cites.
Shicheng Xu, Liang Pang, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, and Jie Zhou · 2024
Later among the works it cites.
Stochastic rag: End-to-end retrieval-augmented generation through expected utility maximization
Hamed Zamani and Michael Bendersky · 2024
Later among the works it cites.
Qingfei Zhao, Ruobing Wang, Yukuo Cen, Daren Zha, Shicheng Tan, Yuxiao Dong, and Jie Tang · 2024
Later among the works it cites.
Seer: Self-aligned evidence extraction for retrieval-augmented generation
Xinping Zhao, Dongfang Li, Yan Zhong, Boren Hu, Yibin Chen, Baotian Hu, and Min Zhang · 2024
Later among the works it cites.
Llamafactory: Unified efficient fine-tuning of 100+ language models
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma · 2024
Later among the works it cites.
Atm: Adversarial tuning multi-agent system makes a robust retrieval-augmented generator
Junda Zhu, Lingyong Yan, Haibo Shi, Dawei Yin, and Lei Sha · 2024
Later among the works it cites.
Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, and Bing Qin · 2024
Later among the works it cites.
Mao-arag: Multi-agent orchestration for adaptive retrieval-augmented generation
Yiqun Chen, Erhan Zhang, Lingyong Yan, Shuaiqiang Wang, Jizhou Huang, Dawei Yin, and Jiaxin Mao · 2025
Closest in time.
Search-r1: Training llms to reason and leverage search engines with reinforcement learning
Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han · 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.