TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios

TableGPT: Few-shot table-to-text generation with table structure reconstruction and content matching

Heng Gong, Yawei Sun, Xiaocheng Feng, Bing Qin, Wei Bi, Xiaojiang Liu, and Ting Liu. 2020 · 1988

Earlier work this paper cites.

Ensemble learning

Thomas G Dietterich et al. 2002 · 2002

Earlier work this paper cites.

Rouge: A package for automatic evaluation of summaries

Chin-Yew Lin. 2004 · 2004

Earlier work this paper cites.

Grappa: Grammar-augmented pre-training for table semantic parsing

Original

Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Bailin Wang, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Richard Socher, and Caiming Xiong. 2020 · 2009

Earlier work this paper cites.

Compositional semantic parsing on semi-structured tables

Panupong Pasupat and Percy Liang. 2015 · 2015

Earlier work this paper cites.

Seq2sql: Generating structured queries from natural language using reinforcement learning

Original

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. 2018 · 2018

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.

TaBERT: Pretraining for joint understanding of textual and tabular data

Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020 · 2020

Earlier work this paper cites.

Tabbie: Pretrained representations of tabular data

Original

Hiroshi Iida, Dung Thai, Varun Manjunatha, and Mohit Iyyer. 2021 · 2021

Earlier work this paper cites.

TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance

Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. 2021 · 2021

Earlier work this paper cites.

Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks

Haoyu Dong, Zhoujun Cheng, et al. 2022 · 2022

Earlier work this paper 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

Earlier work this paper cites.

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, and Dragomir Radev. 2022 · 2022

Cited alongside, same era.

Training language models to follow instructions with human feedback

Long Ouyang, Jeffrey 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 F. Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022

Cited alongside, same era.

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022

Cited alongside, same era.

UnifiedSKG: Unifying and multi-tasking structured knowledge grounding with text-to-text language models

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

Cited alongside, same era.

Din-sql: Decomposed in-context learning of text-to-sql with self-correction

Original

Mohammadreza Pourreza and Davood Rafiei. 2023 · 2023

Later among the works it cites.

Code llama: Open foundation models for code

Original

Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, I. Evtimov, Joanna Bitton, Manish P Bhatt, Cristian Cantón Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre D’efossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve. 2023 · 2023

Later among the works it cites.

Db-gpt: Empowering database interactions with private large language models

Original

Siqiao Xue, Caigao Jiang, Wenhui Shi, Fangyin Cheng, Keting Chen, Hongjun Yang, Zhiping Zhang, Jianshan He, Hongyang Zhang, Ganglin Wei, et al. 2023 · 2023

Later among the works it cites.

Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning

Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li. 2023 · 2023

Later among the works it cites.