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Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 1907
Earlier work this paper cites.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel · 1965
Earlier work this paper cites.
Cosql: A conversational text-to-sql challenge towards cross-domain natural language interfaces to databases
Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander R. Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter S. Lasecki, and Dragomir R. Radev · 1979
Earlier work this paper cites.
Large test collection experiments on an operational, interactive system: Okapi at TREC
Stephen E. Robertson, Steve Walker, and Micheline Hancock-Beaulieu · 1995
Earlier work this paper cites.
Aggregate and mixed-order markov models for statistical language processing, 1997
Lawrence Saul and Fernando Pereira · 1997
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent · 2000
Earlier work this paper cites.
TRANX: A transition-based neural abstract syntax parser for semantic parsing and code generation
Pengcheng Yin and Graham Neubig · 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 · 2004
Earlier work this paper cites.
The magellan data repository
Sanjib Das, AnHai Doan, Paul Suganthan G. C., Chaitanya Gokhale, Pradap Konda, Yash Govind, and Derek Paulsen · 2015
Earlier work this paper cites.
Building the dresden web table corpus: A classification approach
Julian Eberius, Katrin Braunschweig, Markus Hentsch, Maik Thiele, Ahmad Ahmadov, and Wolfgang Lehner · 2015
Earlier work this paper cites.
Character-aware neural language models, 2015
Yoon Kim, Yacine Jernite, David Sontag, and Alexander M. Rush · 2015
Earlier work this paper cites.
Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Wide & deep learning for recommender systems, 2016
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah · 2016
Earlier work this paper cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Entity embeddings of categorical variables, 2016
Cheng Guo and Felix Berkhahn · 2016
Earlier work this paper cites.
The synthetic data vault
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni · 2016
Earlier work this paper cites.
Deepfm: A factorization-machine based neural network for ctr prediction, 2017
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He · 2017
Earlier work this paper cites.
Search-based neural structured learning for sequential question answering
Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Sqlnet: Generating structured queries from natural language without reinforcement learning, 2017
Xiaojun Xu, Chang Liu, and Dawn Song · 2017
Earlier work this paper cites.
Sqlizer: query synthesis from natural language
Navid Yaghmazadeh, Yuepeng Wang, Isil Dillig, and Thomas Dillig · 2017
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Privbayes: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
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Generating multi-label discrete patient records using generative adversarial networks, 2018
Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, and Jimeng Sun · 2018
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Data synthesis based on generative adversarial networks
Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, and Youngmin Kim · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
Earlier work this paper cites.
Regularization learning networks: deep learning for tabular datasets
Ira Shavitt and Eran Segal · 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
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Synthesizing electronic health records using improved generative adversarial networks
Mrinal Kanti Baowaly, Chia-Ching Lin, Chao-Lin Liu, and Kuan-Ta Chen · 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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1d convolutional neural networks and applications: A survey, 2019
Serkan Kiranyaz, Onur Avci, Osama Abdeljaber, Turker Ince, Moncef Gabbouj, and Daniel J. Inman · 2019
Earlier work this paper cites.
Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2019
Earlier work this paper cites.
Neural oblivious decision ensembles for deep learning on tabular data, 2019
Sergei Popov, Stanislav Morozov, and Artem Babenko · 2019
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Catboost: unbiased boosting with categorical features, 2019
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2019
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Machine learning for quantitative finance applications: A survey
Francesco Rundo, Francesca Trenta, Agatino Luigi di Stallo, and Sebastiano Battiato · 2019
Earlier work this paper cites.
Supertml: Two-dimensional word embedding for the precognition on structured tabular data, 2019
Baohua Sun, Lin Yang, Wenhan Zhang, Michael Lin, Patrick Dong, Charles Young, and Jason Dong · 2019
Earlier work this paper cites.
Stock market prediction using machine learning (ml) algorithms
Muhammad Umer, Muhammad Awais, and Muhammad Muzammul · 2019
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Modeling tabular data using conditional gan, 2019
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 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 R. Radev · 2019
Earlier work this paper cites.
Tabnet: Attentive interpretable tabular learning, 2020
Sercan O. Arik and Tomas Pfister · 2020
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Generating synthetic data in finance: opportunities, challenges and pitfalls
Samuel A Assefa, Danial Dervovic, Mahmoud Mahfouz, Robert E Tillman, Prashant Reddy, and Manuela Veloso · 2020
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Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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
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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 · 2020
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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
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INFOTABS: Inference on tables as semi-structured data
Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, and Vivek Srikumar · 2020
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Tapas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Martin Eisenschlos · 2020
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Tabtransformer: Tabular data modeling using contextual embeddings, 2020
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin · 2020
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Net-dnf: Effective deep modeling of tabular data
Liran Katzir, Gal Elidan, and Ran El-Yaniv · 2020
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Vaem: a deep generative model for heterogeneous mixed type data, 2020
Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard Turner, and Cheng Zhang · 2020
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ToTTo: A controlled table-to-text generation dataset
Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das · 2020
Earlier work this paper cites.
Predictive biases in natural language processing models: A conceptual framework and overview
Deven Santosh Shah, H Andrew Schwartz, and Dirk Hovy · 2020
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Generating privacy-preserving synthetic tabular data using oblivious variational autoencoders
L Vivek Harsha Vardhan and Stanley Kok · 2020
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TaBERT: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel · 2020
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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
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Arm-net: Adaptive relation modeling network for structured data
Shaofeng Cai, Kaiping Zheng, Gang Chen, H. V. Jagadish, Beng Chin Ooi, and Meihui Zhang · 2021
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Synthesising multi-modal minority samples for tabular data, 2021
Sajad Darabi and Yotam Elor · 2021
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Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
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Open domain question answering over tables via dense retrieval
Jonathan Herzig, Thomas Mueller, Syrine Krichene, and Julian Eisenschlos · 2021
Cited alongside, same era.
TABBIE: Pretrained representations of tabular data
Hiroshi Iida, Dung Thai, Varun Manjunatha, and Mohit Iyyer · 2021
Cited alongside, same era.
Boost then convolve: Gradient boosting meets graph neural networks, 2021
Sergei Ivanov and Liudmila Prokhorenkova · 2021
Cited alongside, same era.
Well-tuned simple nets excel on tabular datasets, 2021
Arlind Kadra, Marius Lindauer, Frank Hutter, and Josif Grabocka · 2021
Cited alongside, same era.
Dnn2lr: Interpretation-inspired feature crossing for real-world tabular data, 2021
Zhaocheng Liu, Qiang Liu, Haoli Zhang, and Yuntian Chen · 2021
Cited alongside, same era.
A review of using machine learning approaches for precision education
Hui Luan and Chin-Chung Tsai · 2021
How large language models will disrupt data management
Raul Castro Fernandez, Aaron J. Elmore, Michael J. Franklin, Sanjay Krishnan, and Chenhao Tan · 2023
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Tabular and latent space synthetic data generation: a literature review
Joao Fonseca and Fernando Bacao · 2023
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Georgi Ganev and Emiliano De Cristofaro · 2023
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Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson · 2023
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TabMT: Generating tabular data with masked transformers
Manbir S Gulati and Paul F Roysdon · 2023
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Cited alongside, same era.
Sdtr: Soft decision tree regressor for tabular data
Haoran Luo, Fan Cheng, Heng Yu, and Yuqi Yi · 2021
Cited alongside, same era.
Scigen: a dataset for reasoning-aware text generation from scientific tables
Nafise Sadat Moosavi, Andreas Rücklé, Dan Roth, and Iryna Gurevych · 2021
Cited alongside, same era.
Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction
Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, and Degui Zhi · 2021
Cited alongside, same era.
Explainable artificial intelligence for tabular data: A survey
Maria Sahakyan, Zeyar Aung, and Talal Rahwan · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization, 2021
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush · 2021
Cited alongside, same era.
Saint: Improved neural networks for tabular data via row attention and contrastive pre-training, 2021
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, and Tom Goldstein · 2021
Cited alongside, same era.
Tabllm: Few-shot classification of tabular data with large language models
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David A. Sontag · 2023
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Towards better serialization of tabular data for few-shot classification with large language models, 2023
Sukriti Jaitly, Tanay Shah, Ashish Shugani, and Razik Singh Grewal · 2023
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StructGPT: A general framework for large language model to reason over structured data
Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Xin Zhao, and Ji-Rong Wen · 2023
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Generating and imputing tabular data via diffusion and flow-based gradient-boosted trees, 2023
Alexia Jolicoeur-Martineau, Kilian Fatras, and Tal Kachman · 2023
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Codi: Co-evolving contrastive diffusion models for mixed-type tabular synthesis
Chaejeong Lee, Jayoung Kim, and Noseong Park · 2023
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RESDSQL: decoupling schema linking and skeleton parsing for text-to-sql
Haoyang Li, Jing Zhang, Cuiping Li, and Hong Chen · 2023
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Graphix-t5: Mixing pre-trained transformers with graph-aware layers for text-to-sql parsing
Jinyang Li, Binyuan Hui, Reynold Cheng, Bowen Qin, Chenhao Ma, Nan Huo, Fei Huang, Wenyu Du, Luo Si, and Yongbin Li · 2023
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Jarvix: A LLM no code platform for tabular data analysis and optimization
Shangching Liu, Shengkun Wang, Tsungyao Chang, Wenqi Lin, Chung-Wei Hsiung, Yi-Chen Hsieh, Yu-Ping Cheng, Sian-Hong Luo, and Jianwei Zhang · 2023
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Investigating the fairness of large language models for predictions on tabular data
Yanchen Liu, Srishti Gautam, Jiaqi Ma, and Himabindu Lakkaraju · 2023
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Summary of chatgpt-related research and perspective towards the future of large language models
Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, Zihao Wu, Lin Zhao, Dajiang Zhu, Xiang Li, Ning Qiang, Dingang Shen, Tianming Liu, and Bao Ge · 2023
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Matching table metadata with business glossaries using large language models, 2023
Elita Lobo, Oktie Hassanzadeh, Nhan Pham, Nandana Mihindukulasooriya, Dharmashankar Subramanian, and Horst Samulowitz · 2023
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Approximate, adapt, anonymize (3a): a framework for privacy preserving training data release for machine learning, 2023
Tamas Madl, Weijie Xu, Olivia Choudhury, and Matthew Howard · 2023
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Language models are weak learners, 2023
Hariharan Manikandan, Yiding Jiang, and J Zico Kolter · 2023
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Prompt engineering in large language models
Ggaliwango Marvin, Nakayiza Hellen, Daudi Jjingo, and Joyce Nakatumba-Nabende · 2023
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Adapting pretrained language models for solving tabular prediction problems in the electronic health record, 2023
Christopher McMaster, David FL Liew, and Douglas EV Pires · 2023
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Rethinking data augmentation for tabular data in deep learning, 2023
Soma Onishi and Shoya Meguro · 2023
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DIN-SQL: decomposed in-context learning of text-to-sql with self-correction
Mohammadreza Pourreza and Davood Rafiei · 2023
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Exploring the limits of transfer learning with a unified text-to-text transformer, 2023
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2023
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Improving generalization in language model-based text-to-sql semantic parsing: Two simple semantic boundary-based techniques
Daking Rai, Bailin Wang, Yilun Zhou, and Ziyu Yao · 2023
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Testing the limits of unified sequence to sequence LLM pretraining on diverse table data tasks
Soumajyoti Sarkar and Leonard Lausen · 2023
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Curated llm: Synergy of llms and data curation for tabular augmentation in ultra low-data regimes
Nabeel Seedat, Nicolas Huynh, Boris van Breugel, and Mihaela van der Schaar · 2023
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Cpllm: Clinical prediction with large language models
Ofir Ben Shoham and Nadav Rappoport · 2023
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Tabular representation, noisy operators, and impacts on table structure understanding tasks in llms
Ananya Singha, José Cambronero, Sumit Gulwani, Vu Le, and Chris Parnin · 2023
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Tablet: Learning from instructions for tabular data, 2023
Dylan Slack and Sameer Singh · 2023
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Realtabformer: Generating realistic relational and tabular data using transformers
Aivin V Solatorio and Olivier Dupriez · 2023
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The first step is the hardest: Pitfalls of representing and tokenizing temporal data for large language models, 2023
Dimitris Spathis and Fahim Kawsar · 2023
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cTBLS: Augmenting large language models with conversational tables
Anirudh S. Sundar and Larry Heck · 2023
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UL2: unifying language learning paradigms
Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Denny Zhou, Neil Houlsby, and Donald Metzler · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
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Chain-of-thought prompting elicits reasoning in large language models, 2023
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou · 2023
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Detime: Diffusion-enhanced topic modeling using encoder-decoder based llm
Weijie Xu, Wenxiang Hu, Fanyou Wu, and Srinivasan Sengamedu · 2023
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T2g-former: Organizing tabular features into relation graphs promotes heterogeneous feature interaction, 2023
Jiahuan Yan, Jintai Chen, Yixuan Wu, Danny Z. Chen, and Jian Wu · 2023
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Effective distillation of table-based reasoning ability from llms, 2023
Bohao Yang, Chen Tang, Kun Zhao, Chenghao Xiao, and Chenghua Lin · 2023
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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
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Finpt: Financial risk prediction with profile tuning on pretrained foundation models, 2023
Yuwei Yin, Yazheng Yang, Jian Yang, and Qi Liu · 2023
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Unified language representation for question answering over text, tables, and images
Bowen Yu, Cheng Fu, Haiyang Yu, Fei Huang, and Yongbin Li · 2023
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Glm-130b: An open bilingual pre-trained model, 2023
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Peng Zhang, Yuxiao Dong, and Jie Tang · 2023
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Tablegpt: Towards unifying tables, nature language and commands into one GPT
Liangyu Zha, Junlin Zhou, Liyao Li, Rui Wang, Qingyi Huang, Saisai Yang, Jing Yuan, Changbao Su, Xiang Li, Aofeng Su, Tao Zhang, Chen Zhou, Kaizhe Shou, Miao Wang, Wufang Zhu, Guoshan Lu, Chao Ye, Yali Ye, Wentao Ye, Yiming Zhang, Xinglong Deng, Jie Xu, Haobo Wang, Gang Chen, and Junbo Zhao · 2023
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Generative table pre-training empowers models for tabular prediction
Tianping Zhang, Shaowen Wang, Shuicheng Yan, Li Jian, and Qian Liu · 2023
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Large language models are complex table parsers
Bowen Zhao, Changkai Ji, Yuejie Zhang, Wen He, Yingwen Wang, Qing Wang, Rui Feng, and Xiaobo Zhang · 2023
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RobuT: A systematic study of table QA robustness against human-annotated adversarial perturbations
Yilun Zhao, Chen Zhao, Linyong Nan, Zhenting Qi, Wenlin Zhang, Xiangru Tang, Boyu Mi, and Dragomir Radev · 2023
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Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization
Pedro RAS Bassi, Sergio SJ Dertkigil, and Andrea Cavalli · 2024
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Elephants never forget: Testing language models for memorization of tabular data
Sebastian Bordt, Harsha Nori, and Rich Caruana · 2024
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A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, and Xing Xie · 2024
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Towards principled assessment of tabular data synthesis algorithms
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Text-to-sql empowered by large language models: A benchmark evaluation
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Understanding the effects of language-specific class imbalance in multilingual fine-tuning, 2024
Vincent Jung and Lonneke van der Plas · 2024
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Opentab: Advancing large language models as open-domain table reasoners
Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, and George Karypis · 2024
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PET-SQL: A prompt-enhanced two-stage text-to-sql framework with cross-consistency
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Table meets llm: Can large language models understand structured table data? a benchmark and empirical study
Yuan Sui, Mengyu Zhou, Mingjie Zhou, Shi Han, and Dongmei Zhang · 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
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