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Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks.
TabFact: A Large-Scale Dataset for Table-Based Fact Verification
Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2020a · 1909
Earlier work this paper cites.
Web Table Extraction, Retrieval and Augmentation: A Survey
Shuo Zhang and Krisztian Balog. 2020 · 2002
Earlier work this paper cites.
ToTTo: A Controlled Table-To-Text Generation Dataset
Ankur P. Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. 2020 · 2004
Earlier work this paper cites.
TURL: Table Understanding through Representation Learning
Xiang Deng, Huan Sun, Alyssa Lees, You Wu, and Cong Yu. 2020 · 2006
Earlier work this paper cites.
Efficient Transformers: A Survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler. 2022 · 2009
Earlier work this paper cites.
Zhiruo Wang, Haoyu Dong, Ran Jia, Jia Li, Zhiyi Fu, Shi Han, and Dongmei Zhang. 2021 · 2010
Earlier work this paper cites.
Search-Based Neural Structured Learning for Sequential Question Answering. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Vancouver, Canada, 1821–1831
Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang. 2017 · 2017
Earlier work this paper cites.
Attention Is All You Need. In Advances in Neural Information Processing Systems , Vol. 30. Curran Associates, Inc
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Language Models Are Few-Shot Learners. In Advances in Neural Information Processing Systems , Vol. 33. Curran Associates, Inc., 1877–1901
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Dhariwal, et al · 2020
Earlier work this paper cites.
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data. In Findings of the Association for Computational Linguistics: EMNLP 2020 . Association for Computational Linguistics, Online, 1026–1036
Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Yang Wang. 2020b · 2020
Earlier work this paper cites.
TableGPT: Few-Shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching. In Proceedings of the 28th International Conference on Computational Linguistics . International Committee on Computational Linguistics, Barcelona, Spain (Online), 1978–1988
Heng Gong, Yawei Sun, Xiaocheng Feng, Bing Qin, Wei Bi, Xiaojiang Liu, and Ting Liu. 2020 · 2020
Earlier work this paper cites.
TaPas: Weakly Supervised Table Parsing via Pre-Training. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Online, 4320–4333
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
Earlier work this paper cites.
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Online, 8413–8426
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020 · 2020
Earlier work this paper cites.
HTLM: Hyper-Text Pre-Training and Prompting of Language Models
Armen Aghajanyan, Dmytro Okhonko, Mike Lewis, Mandar Joshi, Hu Xu, Gargi Ghosh, and Luke Zettlemoyer. 2021 · 2021
Earlier work this paper cites.
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured Information
Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, and Arpit Mittal. 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, Kaplan, et al · 2021
Cited alongside, same era.
Training Verifiers to Solve Math Word Problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
Cited alongside, same era.
MATE: Multi-View Attention for Table Transformer Efficiency
Julian Martin Eisenschlos, Maharshi Gor, Thomas Müller, and William W. Cohen. 2021 · 2021
Cited alongside, same era.
TruthfulQA: Measuring How Models Mimic Human Falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
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TableFormer: Table Structure Understanding with Transformers
Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, and Peter Staar. 2022 · 2022
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Training Language Models to Follow Instructions with Human Feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Song, et al · 2022
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Hiroshi Iida, Dung Thai, Varun Manjunatha, and Mohit Iyyer. 2021 · 2021
Cited alongside, same era.
MultiModalQA: Complex Question Answering over Text, Tables and Images
Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, and Jonathan Berant. 2021 · 2021
Cited alongside, same era.
Large Language Models Are Few(1)-Shot Table Reasoners
Wenhu Chen. 2022 · 2022
Cited alongside, same era.
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Cited alongside, same era.
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
Haoyu Dong, Zhoujun Cheng, Xinyi He, Mengyu Zhou, Anda Zhou, Fan Zhou, Ao Liu, Shi Han, and Dongmei Zhang. 2022 · 2022
Cited alongside, same era.
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL
Longxu Dou, Yan Gao, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, and Jian-Guang Lou. 2022 · 2022
Cited alongside, same era.
Inferring Tabular Analysis Metadata by Infusing Distribution and Knowledge Information
Xinyi He, Mengyu Zhou, Jialiang Xu, Xiao Lv, Tianle Li, Yijia Shao, Shi Han, Zejian Yuan, and Dongmei Zhang. 2022 · 2022
Cited alongside, same era.
GitTables: A Large-Scale Corpus of Relational Tables
Madelon Hulsebos, Çağatay Demiralp, and Paul Groth. 2022 · 2022
Cited alongside, same era.
Yijia Shao, Mengyu Zhou, Yifan Zhong, Tao Wu, Hongwei Han, Shi Han, Gideon Huang, and Dongmei Zhang. 2022 · 2022
Later among the works it cites.
StruBERT: Structure-aware BERT for Table Search and Matching. In Proceedings of the ACM Web Conference 2022 . ACM
Mohamed Trabelsi, Zhiyu Chen, Shuo Zhang, Brian D. Davison, and Jeff Heflin. 2022 · 2022
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Self-Consistency Improves Chain of Thought Reasoning in Language Models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Later among the works it cites.
Chain of Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
Later among the works it cites.
Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, and Tao Yu. 2022 · 2022
Later among the works it cites.
Let’s Sample Step by Step: Adaptive-Consistency for Efficient Reasoning with LLMs
Pranjal Aggarwal, Aman Madaan, Yiming Yang, and Mausam. 2023 · 2023
Closest in time.
Simulating Users in Interactive Web Table Retrieval. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (Birmingham, United Kingdom) (CIKM ’23) . Association for Computing Machinery, New York, NY, USA, 3875–3879
Björn Engelmann, Timo Breuer, and Philipp Schaer. 2023 · 2023
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OpenAI. 2023 · 2023
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Planning with Large Language Models for Code Generation
Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B. Tenenbaum, and Chuang Gan. 2023 · 2023
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