research-article

DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models

Authors:

Arash Dargahi Nobari,

Davood RafieiAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 2, Issue 1

Article No.: 24, Pages 1 - 24

https://doi.org/10.1145/3639279

Published: 26 March 2024 Publication History

Abstract

Many organizations rely on data from government and third-party sources, and those sources rarely follow the same data formatting. This introduces challenges in integrating data from multiple sources or aligning external sources with internal databases. Commercial database systems do not offer adequate support for integrating data from heterogeneous sources, and manual integration is both time-consuming and inefficient. State-of-the-art data integration approaches that rely on similarity functions and textual transformations often fail to handle challenging cases where multiple mappings are required, or the mappings go beyond simple textual transformations.

In this paper, we study the potentials of deep neural models for transforming tables for joinability. In particular, we cast the problem as a prediction task and develop a framework that leverages large deep-learning language models to transform tabular data from a source formatting to a desired target representation. Our framework can efficiently learn the patterns for mapping a source formatting into an expected target using just a few examples, which can then be used for tasks such as table joining, filling in missing values, and error detection. Compared to state-of-the-art mapping and joining approaches, our framework delivers noticeably more accurate and scalable performance on both real-world and synthetic datasets. Our experimental evaluation also shows that the performance of the proposed framework using our fine-tuned model is at par or better than large language models such as GPT-3, despite the significant difference in size, and that using large language models within our framework improves their performance.

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Cited By

Li PHe YYashar DCui WGe SZhang HRifinski Fainman DZhang DChaudhuri S(2024)Table-GPT: Table Fine-tuned GPT for Diverse Table TasksProceedings of the ACM on Management of Data10.1145/36549792:3(1-28)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654979

Index Terms

DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Data management systems
    1. Information integration
  2. Information retrieval
    1. Retrieval models and ranking
      1. Language models

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 2, Issue 1

SIGMOD

February 2024

1874 pages

EISSN:2836-6573

DOI:10.1145/3654807

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Divyakant Agrawal
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Published: 26 March 2024

Published in PACMMOD Volume 2, Issue 1

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Li PHe YYashar DCui WGe SZhang HRifinski Fainman DZhang DChaudhuri S(2024)Table-GPT: Table Fine-tuned GPT for Diverse Table TasksProceedings of the ACM on Management of Data10.1145/36549792:3(1-28)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654979

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