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Retrieval-Augmented Generation (RAG) offers a promising solution to address various limitations of Large Language Models (LLMs), such as hallucination and difficulties in keeping up with real-time updates.
REALM: retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2002
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The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson and Hugo Zaragoza. 2009 · 2009
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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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, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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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 · 2020
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Employing personal word embeddings for personalized search
Jing Yao, Zhicheng Dou, and Ji-Rong Wen. 2020 · 2020
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Encoding history with context-aware representation learning for personalized search
Yujia Zhou, Zhicheng Dou, and Ji-Rong Wen. 2020 · 2020
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KILT: a benchmark for knowledge intensive language tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel. 2021 · 2021
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Contrastive learning of user behavior sequence for context-aware document ranking
Yutao Zhu, Jian-Yun Nie, Zhicheng Dou, Zhengyi Ma, Xinyu Zhang, Pan Du, Xiaochen Zuo, and Hao Jiang. 2021 · 2021
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Enhancing user behavior sequence modeling by generative tasks for session search
Haonan Chen, Zhicheng Dou, Yutao Zhu, Zhao Cao, Xiaohua Cheng, and Ji-Rong Wen. 2022 · 2022
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Time-aware language models as temporal knowledge bases
Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, and William W. Cohen. 2022 · 2022
Cited alongside, same era.
Internet-augmented language models through few-shot prompting for open-domain question answering
Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, and Nikolai Grigorev. 2022 · 2022
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A category-aware multi-interest model for personalized product search
Jiongnan Liu, Zhicheng Dou, Qiannan Zhu, and Ji-Rong Wen. 2022 · 2022
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Leancontext: Cost-efficient domain-specific question answering using llms
Md. Adnan Arefeen, Biplob Debnath, and Srimat Chakradhar. 2023 · 2023
Cited alongside, same era.
Self-rag: Learning to retrieve, generate, and critique through self-reflection
Investigating the factual knowledge boundary of large language models with retrieval augmentation
Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, and Haifeng Wang. 2023 · 2023
Later among the works it cites.
Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2023 · 2023
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Incorporating explicit subtopics in personalized search
Shuting Wang, Zhicheng Dou, Jing Yao, Yujia Zhou, and Ji-Rong Wen. 2023a · 2023
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C-pack: Packaged resources to advance general chinese embedding
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. 2023 · 2023
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RECOMP: improving retrieval-augmented lms with compression and selective augmentation
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Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023 · 2023
Cited alongside, same era.
Contrastive learning for user sequence representation in personalized product search
Shitong Dai, Jiongnan Liu, Zhicheng Dou, Haonan Wang, Lin Liu, Bo Long, and Ji-Rong Wen. 2023 · 2023
Cited alongside, same era.
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, Qianyu Guo, Meng Wang, and Haofen Wang. 2023 · 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 · 2023
Cited alongside, same era.
RA-DIT: retrieval-augmented dual instruction tuning
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 · 2023
Cited alongside, same era.
RETA-LLM: A retrieval-augmented large language model toolkit
Jiongnan Liu, Jiajie Jin, Zihan Wang, Jiehan Cheng, Zhicheng Dou, and Ji-Rong Wen. 2023 · 2023
Cited alongside, same era.
In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023
Cited alongside, same era.
Integrated personalized and diversified search based on search logs
Jiongnan Liu, Zhicheng Dou, Jian-Yun Nie, and Ji-Rong Wen. 2024a
Cited in the paper.
Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2023 · 2023
Later among the works it cites.
Chain-of-note: Enhancing robustness in retrieval-augmented language models
Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, and Dong Yu. 2023 · 2023
Later among the works it cites.
Large language models for information retrieval: A survey
Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, and Ji-Rong Wen. 2023 · 2023
Later among the works it cites.
Benchmarking large language models in retrieval-augmented generation
Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024 · 2024
Closest in time.
Say more with less: Understanding prompt learning behaviors through gist compression
Xinze Li, Zhenghao Liu, Chenyan Xiong, Shi Yu, Yukun Yan, Shuo Wang, and Ge Yu. 2024 · 2024
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Personalized and diversified: Ranking search results in an integrated way
Shuting Wang, Zhicheng Dou, Jiongnan Liu, Qiannan Zhu, and Ji-Rong Wen. 2024 · 2024
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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 · 2024
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