[13] Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Yu Wu, Sergey Edunov, Danqi Chen, and Wen tau Yih. Dense passage retrieval for open-domain question answering. In EMNLP 2020, 2020.
[14] Omar Khattab and Matei Zaharia. ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. In SIGIR 2020, 2020.
[15] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. 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 V. Le, and Slav Petrov. Natural questions: A benchmark for question answering research. TACL, 7:453–466, 2019.
[16] John Lafferty and ChengXiang Zhai. Document language models, query models, and risk minimization for information retrieval. In SIGIR 2001, 2001.
[17] Hyunjie Lee, Jaeyoung Kim, Hoyeon Chang, Hanseok Oh, Sohee Yang, Vladimir Karpukhin, Yi Lu, and Minjoon Seo. Nonparametric decoding for generative retrieval. In Findings of ACL 2023, 2023.
[18] Jimmy Lin, Rodrigo Nogueira, and Andrew Yates. Pretrained Transformers for Text Ranking: BERT and Beyond. Springer Nature, 2022.
[19] Yuxiang Lu, Yiding Liu, Jiaxiang Liu, Yunsheng Shi, Zhengjie Huang, Shi Feng, Yu Sun, Hao Tian, Hua Wu, Shuaiqiang Wang, Dawei Yin, and Haifeng Wang. ERNIE-Search: Bridging cross-encoder with dual-encoder via self on-the-fly distillation for dense passage retrieval. ArXiv, abs/2205.09153, 2022.
[20] Sanket Vaibhav Mehta, Jai Gupta, Yi Tay, Mostafa Dehghani, Vinh Quang Tran, Jinfeng Rao, Marc-Alexander Najork, Emma Strubell, and Donald Metzler. DSI++: Updating transformer memory with new documents. ArXiv, abs/2212.09744, 2022.
[21] Donald Metzler, Yi Tay, and Dara Bahri. Rethinking search: Making domain experts out of dilettantes. In SIGIR 2021, 2021.
[22] Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant, Ji Ma, Keith B. Hall, Daniel Matthew Cer, and Yinfei Yang. Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models. In Findings of ACL 2022, 2022.
[23] Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, and Yinfei Yang. Large dual encoders are generalizable Retrievers. In EMNLP, 2022.
[24] Rodrigo Nogueiral and Jimmy Lin. From doc2query to doc1ttttquery. https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTQuery-v2.pdf, 2019.
[25] Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vassilis Plachouras, Tim Rocktaschel, and Sebastian Riedel. KILT: A benchmark for knowledge intensive language tasks. In NAACL 2021, 2021.
[26] Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering. In NAACL 2021, 2021.
[27] Colin Raffel, Noam M. Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 2020.
[28] Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H. Keshavan, Trung Hieu Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, and Maheswaran Sathiamoorthy. Recommender systems with generative retrieval. In NeurIPS 2023, 2023.