Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples
Summary: Auto‑Tables auto-synthesizes multi-step pipelines (e.g., Python) to convert non‑relational “wild” tables into canonical row‑entity / column‑attribute relational form without requiring examples. On a 244-case benchmark it solves >70% at interactive latency, offering zero‑example end‑to‑end table preparation. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Peng Li (Georgia Institute of Technology)
- 2. Yeye He (Microsoft)
- 3. Cong Yan (Microsoft)
- 4. Yue Wang (Microsoft)
- 5. Surajit Chaudhuri (Microsoft)
BibTeX Citation
@article{li_vldb23,
title = {{Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples}},
author = {Li, Peng and He, Yeye and Yan, Cong and Wang, Yue and Chaudhuri, Surajit},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {11},
pages = {3391--3403},
doi = {10.14778/3611479.3611534},
url = {https://doi.org/10.14778/3611479.3611534},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 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 |
| 8,797 | Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations | 2024 | SIGMOD | 5.3702298e-05 |
| 9,380 | Auto-Prep: Holistic Prediction of Data Preparation Steps for Self-Service Business Intelligence | 2025 | VLDB | 5.2755515e-05 |
| 10,202 | BAT: Target-Instance-Free Data Preparation Synthesis via LLM-Driven Tree Search | 2026 | SIGMOD | 5.093636e-05 |
| 10,931 | AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework | 2025 | VLDB | 5.093636e-05 |
| 11,307 | Utopia: Automatic Pivot Table Assistant | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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