Fetching the paper…
Reading the bibliography…
Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task-specific perturbations, e.g., replacing key question entities or shuffling table columns.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
Earlier work this paper cites.
Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
Earlier work this paper cites.
A large public corpus of web tables containing time and context metadata
Oliver Lehmberg, Dominique Ritze, Robert Meusel, and Christian Bizer. 2016 · 2016
Earlier work this paper cites.
Search-based neural structured learning for sequential question answering
Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang. 2017 · 2017
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.
Adversarial tableqa: Attention supervision for question answering on tables
Minseok Cho, Reinald Kim Amplayo, Seung won Hwang, and Jonghyuck Park. 2018 · 2018
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. 2018 · 2018
Earlier work this paper cites.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. 2019 · 2019
Earlier work this paper cites.
SParC: Cross-domain semantic parsing in context
Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, and Dragomir Radev. 2019 · 2019
Earlier work this paper cites.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. 2019 · 2019
Earlier work this paper cites.
Beat the AI: Investigating adversarial human annotation for reading comprehension
Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp. 2020 · 2020
Earlier work this paper cites.
Understanding tables with intermediate pre-training
Julian Eisenschlos, Syrine Krichene, and Thomas Müller. 2020 · 2020
Earlier work this paper cites.
INFOTABS: Inference on tables as semi-structured data
Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, and Vivek Srikumar. 2020 · 2020
Earlier work this paper cites.
TaPas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
Earlier work this paper cites.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Earlier work this paper cites.
BERT-ATTACK: Adversarial attack against BERT using BERT
Linyang Li, Ruotian Ma, Qipeng Guo, Xiangyang Xue, and Xipeng Qiu. 2020 · 2020
Cited alongside, same era.
Exploring unexplored generalization challenges for cross-database semantic parsing
Alane Suhr, Ming-Wei Chang, Peter Shaw, and Kenton Lee. 2020 · 2020
Cited alongside, same era.
DuSQL: A large-scale and pragmatic Chinese text-to-SQL dataset
Lijie Wang, Ao Zhang, Kun Wu, Ke Sun, Zhenghua Li, Hua Wu, Min Zhang, and Haifeng Wang. 2020b · 2020
Cited alongside, same era.
TaBERT: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020 · 2020
Cited alongside, same era.
Photon: A robust cross-domain text-to-SQL system
Jichuan Zeng, Xi Victoria Lin, Steven C.H. Hoi, Richard Socher, Caiming Xiong, Michael Lyu, and Irwin King. 2020 · 2020
Cited alongside, same era.
Generating semantically valid adversarial questions for tableqa
Promptsource: An integrated development environment and repository for natural language prompts
Stephen H Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, et al. 2022 · 2022
Later among the works it cites.
Large language models are few(1)-shot table reasoners
Wenhu Chen. 2022 · 2022
Later among the works it cites.
HiTab: A hierarchical table dataset for question answering and natural language generation
Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, and Dongmei Zhang. 2022 · 2022
Later among the works it cites.
Right for the right reason: Evidence extraction for trustworthy tabular reasoning
Vivek Gupta, Shuo Zhang, Alakananda Vempala, Yujie He, Temma Choji, and Vivek Srikumar. 2022 · 2022
Later among the works it cites.
OmniTab: Pretraining with natural and synthetic data for few-shot table-based question answering
Zhengbao Jiang, Yi Mao, Pengcheng He, Graham Neubig, and Weizhu Chen. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yi Zhu, Yiwei Zhou, and Menglin Xia. 2020 · 2020
Cited alongside, same era.
FEVEROUS: Fact extraction and VERification over unstructured and structured information
Rami Aly, Zhijiang Guo, Michael Sejr Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, and Arpit Mittal. 2021 · 2021
Cited alongside, same era.
Robustness and adversarial examples in natural language processing
Kai-Wei Chang, He He, Robin Jia, and Sameer Singh. 2021 · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021
Cited alongside, same era.
Towards robustness of text-to-SQL models against synonym substitution
Yujian Gan, Xinyun Chen, Qiuping Huang, Matthew Purver, John R. Woodward, Jinxia Xie, and Pengsheng Huang. 2021 · 2021
Cited alongside, same era.
Robustness gym: Unifying the NLP evaluation landscape
Karan Goel, Nazneen Fatema Rajani, Jesse Vig, Zachary Taschdjian, Mohit Bansal, and Christopher Ré. 2021 · 2021
Cited alongside, same era.
Chase: A large-scale and pragmatic Chinese dataset for cross-database context-dependent text-to-SQL
Jiaqi Guo, Ziliang Si, Yu Wang, Qian Liu, Ming Fan, Jian-Guang Lou, Zijiang Yang, and Ting Liu. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
TAPEX: Table pre-training via learning a neural SQL executor
Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, and Jian-Guang Lou. 2022 · 2022
Later among the works it cites.
Towards robustness of text-to-SQL models against natural and realistic adversarial table perturbation
Xinyu Pi, Bing Wang, Yan Gao, Jiaqi Guo, Zhoujun Li, and Jian-Guang Lou. 2022 · 2022
Later among the works it cites.
Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
Later among the works it cites.
Identifying and mitigating spurious correlations for improving robustness in NLP models
Tianlu Wang, Rohit Sridhar, Diyi Yang, and Xuezhi Wang. 2022a · 2022
Later among the works it cites.
Measure and improve robustness in NLP models: A survey
Xuezhi Wang, Haohan Wang, and Diyi Yang. 2022b · 2022
Later among the works it cites.
TableFormer: Robust transformer modeling for table-text encoding
Jingfeng Yang, Aditya Gupta, Shyam Upadhyay, Luheng He, Rahul Goel, and Shachi Paul. 2022 · 2022
Later among the works it cites.
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, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
Later among the works it cites.
ReasTAP: Injecting table reasoning skills during pre-training via synthetic reasoning examples
Yilun Zhao, Linyong Nan, Zhenting Qi, Rui Zhang, and Dragomir Radev. 2022b · 2022
Later among the works it cites.
Dr.spider: A diagnostic evaluation benchmark towards text-to-SQL robustness
Shuaichen Chang, Jun Wang, Mingwen Dong, Lin Pan, Henghui Zhu, Alexander Hanbo Li, Wuwei Lan, Sheng Zhang, Jiarong Jiang, Joseph Lilien, et al. 2023 · 2023
Closest in time.