Large Language Models as Data Preprocessors

Magellan: toward building entity matching management systems over data science stacks

P. Konda, S. Das, A. Doan, A. Ardalan, J. R. Ballard, H. Li, F. Panahi, H. Zhang, J. Naughton, S. Prasad, et al · 2016

Earlier work this paper cites.

HoloClean: Holistic data repairs with probabilistic inference

T. Rekatsinas, X. Chu, I. F. Ilyas, and C. Ré · 2017

Earlier work this paper cites.

HoloDetect: Few-shot learning for error detection

A. Heidari, J. McGrath, I. F. Ilyas, and T. Rekatsinas · 2019

Earlier work this paper cites.

Language models are few-shot learners

T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020

Earlier work this paper cites.

Deep entity matching with pre-trained language models

Y. Li, J. Li, Y. Suhara, A. Doan, and W.-C. Tan · 2020

Earlier work this paper cites.

Capturing semantics for imputation with pre-trained language models

Y. Mei, S. Song, C. Fang, H. Yang, J. Fang, and J. Long · 2021

Earlier work this paper cites.

Language models as or for knowledge bases

Original

S. Razniewski, A. Yates, N. Kassner, and G. Weikum · 2021

Earlier work this paper cites.

Smat: An attention-based deep learning solution to the automation of schema matching

J. Zhang, B. Shin, J. D. Choi, and J. C. Ho · 2021

Cited alongside, same era.

Large language models are zero-shot reasoners

Original

T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa · 2022

Cited alongside, same era.

Can foundation models wrangle your data?

A. Narayan, I. Chami, L. Orr, and C. Ré · 2022

Cited alongside, same era.

Sentence bert

N. Reimers · 2022

Cited alongside, same era.

Codexdb: Synthesizing code for query processing from natural language instructions using gpt-3 codex

I. Trummer · 2022

Cited alongside, same era.

Chain of thought prompting elicits reasoning in large language models

Original

Batch prompting: Efficient inference with large language model APIs

Original

Z. Cheng, J. Kasai, and T. Yu · 2023

Closest in time.

DB-GPT: Revolutionizing database interactions with private LLM technology, 2023

csunny · 2023

Closest in time.

Table-GPT: Table-tuned GPT for diverse table tasks

Original

P. Li, Y. He, D. Yashar, W. Cui, S. Ge, H. Zhang, D. R. Fainman, D. Zhang, and S. Chaudhuri · 2023

Closest in time.

Unicorn: A unified multi-tasking model for supporting matching tasks in data integration

J. Tu, J. Fan, N. Tang, P. Wang, G. Li, X. Du, X. Jia, and S. Gao · 2023

Closest in time.

A survey of large language models

Original

W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al · 2023

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