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Column type annotation is the task of annotating the columns of a relational table with the semantic type of the values contained in each column.
Language models are few-shot learners,
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Profiling the potential of web tables for augmenting cross-domain knowledge bases,
D. Ritze, O. Lehmberg, Y. Oulabi, C. Bizer, · 2016
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Attention is all you need,
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, et al., · 2017
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,
J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, · 2019
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RoBERTa: A Robustly Optimized BERT Pretraining Approach,
Y. Liu, M. Ott, N. Goyal, J. Du, et al., · 2019
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Sherlock: A deep learning approach to semantic data type detection,
M. Hulsebos, K. Hu, M. Bakker, E. Zgraggen, et al., · 2019
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Dataset search: a survey,
A. Chapman, E. Simperl, L. Koesten, G. Konstantinidis, et al., · 2020
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SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems,
E. Jiménez-Ruiz, O. Hassanzadeh, V. Efthymiou, J. Chen, et al., · 2020
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TURL: Table understanding through representation learning,
X. Deng, H. Sun, A. Lees, Y. Wu, et al., · 2020
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Sato: Contextual semantic type detection in tables,
D. Zhang, M. Hulsebos, Y. Suhara, c. Demiralp, et al., · 2020
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TinyBERT: Distilling BERT for natural language understanding,
X. Jiao, Y. Yin, L. Shang, X. Jiang, et al., · 2020
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TABBIE: Pretrained representations of tabular data,
H. Iida, D. Thai, V. Manjunatha, M. Iyyer, · 2021
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Calibrate before use: Improving few-shot performance of language models,
Z. Zhao, E. Wallace, S. Feng, D. Klein, et al., · 2021
Cited alongside, same era.
Tcn: Table convolutional network for web table interpretation,
D. Wang, P. Shiralkar, C. Lockard, B. Huang, et al., · 2021
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Smat: An attention-based deep learning solution to the automation of schema matching,
J. Zhang, B. Shin, J. D. Choi, J. C. Ho, · 2021
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Annotating columns with pre-trained language models,
Y. Suhara, J. Li, Y. Li, D. Zhang, et al., · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback,
L. Ouyang, J. Wu, X. Jiang, D. Almeida, et al., · 2022
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,
J. Wei, X. Wang, D. Schuurmans, M. Bosma, et al., · 2022
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Transformers for tabular data representation: a tutorial on models and applications,
G. Badaro, P. Papotti, · 2022
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Generic entity resolution models,
J. Tang, Y. Zuo, L. Cao, S. Madden, · 2022
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Instruction induction: From few examples to natural language task descriptions,
O. Honovich, U. Shaham, S. R. Bowman, O. Levy, · 2022
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From tabular data to knowledge graphs: A survey of semantic table interpretation tasks and methods,
J. Liu, Y. Chabot, R. Troncy, V.-P. Huynh, et al., · 2023
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Data lakes: A survey of functions and systems,
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A. Chowdhery, S. Narang, J. Devlin, M. Bosma, et al., · 2022
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Bloom+ 1: Adding language support to bloom for zero-shot prompting,
Z.-X. Yong, H. Schoelkopf, N. Muennighoff, A. F. Aji, et al., · 2022
Cited alongside, same era.
Emergent abilities of large language models,
J. Wei, Y. Tay, R. Bommasani, C. Raffel, et al., · 2022
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Can foundation models wrangle your data?,
A. Narayan, I. Chami, L. Orr, C. Ré, · 2022
Cited alongside, same era.
Can language models automate data wrangling?,
G. Jaimovitch-López, C. Ferri, J. Hernández-Orallo, F. Martínez-Plumed, et al., · 2022
Cited alongside, same era.
Sotab: The wdc schema. org table annotation benchmark,
K. Korini, R. Peeters, C. Bizer, · 2022
Cited alongside, same era.
Do prompt-based models really understand the meaning of their prompts?,
A. Webson, E. Pavlick, · 2022
Cited alongside, same era.
R. Hai, C. Koutras, C. Quix, M. Jarke, · 2023
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A survey of large language models,
W. X. Zhao, K. Zhou, J. Li, T. Tang, et al., · 2023
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Reca: Related tables enhanced column semantic type annotation framework,
Y. Sun, H. Xin, L. Chen, · 2023
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Using chatgpt for entity matching,
R. Peeters, C. Bizer, · 2023
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A survey on in-context learning,
Q. Dong, L. Li, D. Dai, C. Zheng, et al., · 2023
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Tabllm: Few-shot classification of tabular data with large language models,
S. Hegselmann, A. Buendia, H. Lang, M. Agrawal, et al., · 2023
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