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Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification.
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Bleu: a method for automatic evaluation of machine translation
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Webtables: Exploring the power of tables on the web
Michael J. Cafarella, Alon Halevy, Daisy Zhe Wang, Eugene Wu, and Yang Zhang · 2008
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang · 2015
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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 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Handling divergent reference texts when evaluating table-to-text generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, and William Cohen · 2019
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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
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Understanding tables with intermediate pre-training
Julian Eisenschlos, Syrine Krichene, and Thomas Müller · 2020
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TaPas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos · 2020
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On the potential of lexico-logical alignments for semantic parsing to sql queries
Tianze Shi, Chen Zhao, Jordan Boyd-Graber, Hal Daumé III, and Lillian Lee · 2020
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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 · 2021
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TUTA: Tree-based transformers for generally structured table pre-training
Zhiruo Wang, Haoyu Dong, Ran Jia, Jia Li, Zhiyi Fu, Shi Han, and Dongmei Zhang · 2021
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Table-to-text generation and pre-training with TabT5
Ewa Andrejczuk, Julian Eisenschlos, Francesco Piccinno, Syrine Krichene, and Yasemin Altun · 2022
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
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Binding language models in symbolic languages
Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, et al · 2022
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PASTA: Table-operations aware fact verification via sentence-table cloze pre-training
Zihui Gu, Ju Fan, Nan Tang, Preslav Nakov, Xiaoman Zhao, and Xiaoyong Du · 2022
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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
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Large language models are few(1)-shot table reasoners
Wenhu Chen · 2023
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PAL: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
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Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu · 2023
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Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes
Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister · 2023
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MathPrompter: Mathematical reasoning using large language models
Shima Imani, Liang Du, and Harsh Shrivastava · 2023
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A survey on table question answering: recent advances
Nengzheng Jin, Joanna Siebert, Dongfang Li, and Qingcai Chen · 2022
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Decomposed prompting: A modular approach for solving complex tasks
Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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FeTaQA: Free-form table question answering
Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, and Dragomir Radev · 2022
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Evaluating the text-to-sql capabilities of large language models
Nitarshan Rajkumar, Raymond Li, and Dzmitry Bahdanau · 2022
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Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Ziqi Jin and Wei Lu · 2023
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A survey on deep learning approaches for text-to-sql
George Katsogiannis-Meimarakis and Georgia Koutrika · 2023
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Benchmarking large language model capabilities for conditional generation
Joshua Maynez, Priyanka Agrawal, and Sebastian Gehrmann · 2023
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Lever: Learning to verify language-to-code generation with execution
Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-tau Yih, Sida Wang, and Xi Victoria Lin · 2023
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OpenAI · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li · 2023
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