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Tabular data are crucial in many fields and their understanding by large language models (LLMs) under high parameter efficiency paradigm is important.
Kgpt: Knowledge-grounded pre-training for data-to-text generation
Wenhu Chen, Yu Su, Xifeng Yan, and William Yang Wang. 2020a · 2010
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Kgpt: Knowledge-grounded pre-training for data-to-text generation
Wenhu Chen, Yu Su, Xifeng Yan, and William Yang Wang. 2020a · 2010
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
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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
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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
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HybridQA: A dataset of multi-hop question answering over tabular and textual data
Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Yang Wang. 2020c · 2020
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Turl: table understanding through representation learning
Xiang Deng, Huan Sun, Alyssa Lees, You Wu, and Cong Yu. 2020 · 2020
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Tapas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Mueller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
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Table2analysis: Modeling and recommendation of common analysis patterns for multi-dimensional data
Mengyu Zhou, Wang Tao, Ji Pengxin, Han Shi, and Zhang Dongmei. 2020 · 2020
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HybridQA: A dataset of multi-hop question answering over tabular and textual data
Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Yang Wang. 2020c · 2020
Earlier work this paper cites.
Turl: table understanding through representation learning
Xiang Deng, Huan Sun, Alyssa Lees, You Wu, and Cong Yu. 2020 · 2020
Earlier work this paper cites.
Tapas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Mueller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
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Table2analysis: Modeling and recommendation of common analysis patterns for multi-dimensional data
Mengyu Zhou, Wang Tao, Ji Pengxin, Han Shi, and Zhang Dongmei. 2020 · 2020
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Mate: Multi-view attention for table transformer efficiency
Julian Eisenschlos, Maharshi Gor, Thomas Mueller, and William Cohen. 2021 · 2021
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Tabbie: Pretrained representations of tabular data
Hiroshi Iida, Dung Thai, Varun Manjunatha, and Mohit Iyyer. 2021 · 2021
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Dot: An efficient double transformer for nlp tasks with tables
Syrine Krichene, Thomas Müller, and Julian Martin Eisenschlos. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Tapex: Table pre-training via learning a neural sql executor
Qian Liu, Bei Chen, Jiaqi Guo, Zeqi Lin, and Jian-guang Lou. 2021 · 2021
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Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 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 · 2021
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Table2charts: Recommending charts by learning shared table representations
Mengyu Zhou, Qingtao Li, Xinyi He, Yuejiang Li, Yibo Liu, Wei Ji, Shi Han, Yining Chen, Daxin Jiang, and Dongmei Zhang. 2021 · 2021
Cited alongside, same era.
Mate: Multi-view attention for table transformer efficiency
Julian Eisenschlos, Maharshi Gor, Thomas Mueller, and William Cohen. 2021 · 2021
Cited alongside, same era.
Tabbie: Pretrained representations of tabular data
Hiroshi Iida, Dung Thai, Varun Manjunatha, and Mohit Iyyer. 2021 · 2021
Cited alongside, same era.
Dot: An efficient double transformer for nlp tasks with tables
Syrine Krichene, Thomas Müller, and Julian Martin Eisenschlos. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Tablellama: Towards open large generalist models for tables
Tianshu Zhang, Xiang Yue, Yifei Li, and Huan Sun. 2023 · 2023
Later among the works it cites.
AnaMeta: A table understanding dataset of field metadata knowledge shared by multi-dimensional data analysis tasks
Xinyi He, Mengyu Zhou, Mingjie Zhou, Jialiang Xu, Xiao Lv, Tianle Li, Yijia Shao, Shi Han, Zejian Yuan, and Dongmei Zhang. 2023 · 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, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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Qian Liu, Bei Chen, Jiaqi Guo, Zeqi Lin, and Jian-guang Lou. 2021 · 2021
Cited alongside, same era.
Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Table2charts: Recommending charts by learning shared table representations
Mengyu Zhou, Qingtao Li, Xinyi He, Yuejiang Li, Yibo Liu, Wei Ji, Shi Han, Yining Chen, Daxin Jiang, and Dongmei Zhang. 2021 · 2021
Cited alongside, same era.
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
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.
LoRA: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Tablellama: Towards open large generalist models for tables
Tianshu Zhang, Xiang Yue, Yifei Li, and Huan Sun. 2023 · 2023
Later among the works it cites.
Deepseek llm: Scaling open-source language models with longtermism
DeepSeek-AI. 2024 · 2024
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Spreadsheetllm: Encoding spreadsheets for large language models
Haoyu Dong, Yuzhang Tian, Jianbo Zhao, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, and Dongmei Zhang. 2024 · 2024
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CoCoST: Automatic complex code generation with online searching and correctness testing
Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou, Shi Han, Zejian Yuan, and Dongmei Zhang. 2024b · 2024
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Table-gpt: Table fine-tuned gpt for diverse table tasks
Peng Li, Yeye He, Dror Yashar, Weiwei Cui, Song Ge, Haidong Zhang, Danielle Rifinski Fainman, Dongmei Zhang, and Surajit Chaudhuri. 2024b · 2024
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Gpt understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2024 · 2024
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TAP4LLM: Table provider on sampling, augmenting, and packing semi-structured data for large language model reasoning
Yuan Sui, Jiaru Zou, Mengyu Zhou, Xinyi He, Lun Du, Shi Han, and Dongmei Zhang. 2024b · 2024
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Chain-of-table: Evolving tables in the reasoning chain for table understanding
Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, and Tomas Pfister. 2024 · 2024
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Tablellm: Enabling tabular data manipulation by llms in real office usage scenarios
Xiaokang Zhang, Jing Zhang, Zeyao Ma, Yang Li, Bohan Zhang, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, et al. 2024 · 2024
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Structlm: Towards building generalist models for structured knowledge grounding
Alex Zhuang, Ge Zhang, Tianyu Zheng, Xinrun Du, Junjie Wang, Weiming Ren, Stephen W. Huang, Jie Fu, Xiang Yue, and Wenhu Chen. 2024 · 2024
Later among the works it cites.
Deepseek llm: Scaling open-source language models with longtermism
DeepSeek-AI. 2024 · 2024
Later among the works it cites.
Spreadsheetllm: Encoding spreadsheets for large language models
Haoyu Dong, Yuzhang Tian, Jianbo Zhao, Junyu Xiong, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, and Dongmei Zhang. 2024 · 2024
Later among the works it cites.
CoCoST: Automatic complex code generation with online searching and correctness testing
Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou, Shi Han, Zejian Yuan, and Dongmei Zhang. 2024b · 2024
Later among the works it cites.
Table-gpt: Table fine-tuned gpt for diverse table tasks
Peng Li, Yeye He, Dror Yashar, Weiwei Cui, Song Ge, Haidong Zhang, Danielle Rifinski Fainman, Dongmei Zhang, and Surajit Chaudhuri. 2024b · 2024
Later among the works it cites.
Gpt understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2024 · 2024
Later among the works it cites.
TAP4LLM: Table provider on sampling, augmenting, and packing semi-structured data for large language model reasoning
Yuan Sui, Jiaru Zou, Mengyu Zhou, Xinyi He, Lun Du, Shi Han, and Dongmei Zhang. 2024b · 2024
Later among the works it cites.
Chain-of-table: Evolving tables in the reasoning chain for table understanding
Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, and Tomas Pfister. 2024 · 2024
Later among the works it cites.
Tablellm: Enabling tabular data manipulation by llms in real office usage scenarios
Xiaokang Zhang, Jing Zhang, Zeyao Ma, Yang Li, Bohan Zhang, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, et al. 2024 · 2024
Later among the works it cites.
Structlm: Towards building generalist models for structured knowledge grounding
Alex Zhuang, Ge Zhang, Tianyu Zheng, Xinrun Du, Junjie Wang, Weiming Ren, Stephen W. Huang, Jie Fu, Xiang Yue, and Wenhu Chen. 2024 · 2024
Later among the works it cites.