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Large language models (LLMs) enable zero-shot approaches in open-domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever.
The TREC-8 question answering track
Ellen M. Voorhees and Dawn M. Tice. 2000 · 2000
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The probabilistic relevance framework: Bm25 and beyond
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Semantic parsing on Freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
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Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Dan Roth. 2017 · 2017
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 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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Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
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Learning to attend on essential terms: An enhanced retriever-reader model for open-domain question answering
Jianmo Ni, Chenguang Zhu, Weizhu Chen, and Julian McAuley. 2019 · 2019
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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 · 2020
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Cicero Nogueira dos Santos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, and Bing Xiang. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave. 2021 · 2021
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Text-to-text multi-view learning for passage re-ranking
Jia-Huei Ju, Jheng-Hong Yang, and Chuan-Ju Wang. 2021 · 2021
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Read before generate! faithful long form question answering with machine reading
Dan Su, Xiaoguang Li, Jindi Zhang, Lifeng Shang, Xin Jiang, Qun Liu, and Pascale Fung. 2022 · 2022
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le. 2022 · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2022
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Discrete prompt optimization via constrained generation for zero-shot re-ranker
Sukmin Cho, Soyeong Jeong, Jeongyeon Seo, and Jong C. Park. 2023 · 2023
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Expand, rerank, and retrieve: Query reranking for open-domain question answering
Yung-Sung Chuang, Wei Fang, Shang-Wen Li, Wen-tau Yih, and James R. Glass. 2023 · 2023
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BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models
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Scaling instruction-finetuned language models
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Realtime QA: what’s the answer right now?
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Huge frozen language models as readers for open-domain question answering
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Improving passage retrieval with zero-shot question generation
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Multitask prompted training enables zero-shot task generalization
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Large language models can be easily distracted by irrelevant context
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Generate rather than retrieve: Large language models are strong context generators
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Challenges in generalization in open domain question answering
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Adversarial examples for evaluating reading comprehension systems
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