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Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
Earlier work this paper cites.
Finding related tables in data lakes for interactive data science
Yi Zhang and Zachary G. Ives. 2020 · 1966
Earlier work this paper cites.
Network flow and testing graph connectivity
Shimon Even and R Endre Tarjan. 1975 · 1975
Earlier work this paper cites.
Data lake management: challenges and opportunities
Fatemeh Nargesian, Erkang Zhu, Renée J Miller, Ken Q Pu, and Patricia C Arocena. 2019 · 1989
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2005
Earlier work this paper cites.
Open question answering over tables and text
Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Wang, and William W Cohen. 2020a · 2010
Earlier work this paper cites.
Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
Earlier work this paper cites.
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. 2018a · 2018
Earlier work this paper cites.
Josie: Overlap set similarity search for finding joinable tables in data lakes
Erkang Zhu, Dong Deng, Fatemeh Nargesian, and Renée J. Miller. 2019 · 2019
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Open domain question answering over tables via dense retrieval
Jonathan Herzig, Thomas Müller, Syrine Krichene, and Julian Eisenschlos. 2021 · 2021
Cited alongside, same era.
KaggleDBQA: Realistic evaluation of text-to-SQL parsers
Chia-Hsuan Lee, Oleksandr Polozov, and Matthew Richardson. 2021 · 2021
Cited alongside, same era.
Joint verification and reranking for open fact checking over tables
Michael Sejr Schlichtkrull, Vladimir Karpukhin, Barlas Oguz, Mike Lewis, Wen-tau Yih, and Sebastian Riedel. 2021 · 2021
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Detecting hallucinated content in conditional neural sequence generation
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Cited alongside, same era.
Table retrieval may not necessitate table-specific model design
Zhiruo Wang, Zhengbao Jiang, Eric Nyberg, and Graham Neubig. 2022 · 2022
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A survey of knowledge-enhanced text generation
Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, and Meng Jiang. 2022 · 2022
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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 · 2023
Later among the works it cites.
A comprehensive evaluation of chatgpt’s zero-shot text-to-sql capability
Aiwei Liu, Xuming Hu, Lijie Wen, and Philip S. Yu. 2023 · 2023
Later among the works it cites.
Linyong Nan, Yilun Zhao, Weijin Zou, Narutatsu Ri, Jaesung Tae, Ellen Zhang, Arman Cohan, and Dragomir Radev. 2023 · 2023
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Mixed-modality representation learning and pre-training for joint table-and-text retrieval in openqa
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Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021 · 2022
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End-to-end table question answering via retrieval-augmented generation
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Table search using a deep contextualized language model
Zhiyu Chen, Mohamed Trabelsi, Jeff Heflin, Yinan Xu, and Brian D Davison. 2020b
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Jinyang Li, Binyuan Hui, Ge Qu, Binhua Li, Jiaxi Yang, Bowen Li, Bailin Wang, Bowen Qin, Rongyu Cao, Ruiying Geng, Nan Huo, Xuanhe Zhou, Chenhao Ma, Guoliang Li, Kevin C. C. Chang, Fei Huang, Reynold Cheng, and Yongbin Li. 2023a
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Jinyang Li, Binyuan Hui, Ge Qu, Binhua Li, Jiaxi Yang, Bowen Li, Bailin Wang, Bowen Qin, Rongyu Cao, Ruiying Geng, et al. 2023b
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Later among the works it cites.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023 · 2023
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Text-to-sql empowered by large language models: A benchmark evaluation
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