DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models
Summary: DTT: an example-driven tabular transformer for joinability across heterogeneous formats. Few-shot mappings learned by fine-tuned LLMs yield accurate joins, missing-values, and error detection; outperform traditional approaches and rival GPT-3. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Arash Dargahi Nobari (University of Alberta)
- 2. Davood Rafiei (University of Alberta)
BibTeX Citation
@inproceedings{nobari_sigmod24,
title = {{DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models}},
author = {Nobari, Arash Dargahi and Rafiei, Davood},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639279},
url = {https://dl.acm.org/doi/10.1145/3639279},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,099 | Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks | 2024 | SIGMOD | 9.1682353e-05 |
| 9,380 | Auto-Prep: Holistic Prediction of Data Preparation Steps for Self-Service Business Intelligence | 2025 | VLDB | 5.2755515e-05 |
| 9,505 | TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations | 2025 | VLDB | 5.258497e-05 |
| 10,856 | Optimized Batch Prompting for Cost-effective LLMs | 2025 | VLDB | 5.093636e-05 |
| 10,867 | Weak-to-Strong Prompts with Lightweight-to-Powerful LLMs for High-Accuracy, Low-Cost, and Explainable Data Transformation | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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