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Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks
Summary: Table-GPT introduces table fine-tuning for GPT-3.5/ChatGPT: continue-training on synthetic tasks distilled from real relational tables to improve 2D table understanding. Yields consistent gains on data transformation/cleaning/imputation/table-QA, including unseen holdout tasks, while preserving instruction-following generalization.
(summarized by gpt-5.4-mini on May 24 2026)
- Paper ID
- 6939
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 8.4924618e-05
- Overall Rank
- 2,587 | 82.01%
- DOI
-
10.1145/3654979
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
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Citing Paper |
Year |
Venue |
Pagerank |
| 5,023 |
GenRewrite: Query Rewriting via Large Language Models |
2026 |
SIGMOD |
5.75363e-05 |
| 7,048 |
Magneto: Combining Small and Large Language Models for Schema Matching |
2025 |
VLDB |
4.8520651e-05 |
| 7,139 |
Automated Validating and Fixing of Text-to-SQL Translation with Execution Consistency |
2025 |
SIGMOD |
4.821174e-05 |
| 8,204 |
ELEET: Efficient Learned Query Execution over Text and Tables |
2024 |
VLDB |
4.5594273e-05 |
| 8,488 |
Can Large Language Models Be Query Optimizer for Relational Databases? |
2026 |
SIGMOD |
4.4998609e-05 |
| 8,736 |
Unveiling Challenges for LLMs in Enterprise Data Engineering |
2026 |
VLDB |
4.456315e-05 |
| 9,371 |
Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations |
2024 |
SIGMOD |
4.3480692e-05 |
| 9,399 |
TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations |
2025 |
VLDB |
4.3441378e-05 |
| 9,479 |
Data Imputation with Limited Data Redundancy Using Data Lakes |
2025 |
VLDB |
4.3341665e-05 |
| 9,994 |
BridgeScope: A Universal Toolkit for Bridging Large Language Models and Databases |
2026 |
CIDR |
4.1945683e-05 |
| 10,109 |
Retrieve-and-Verify: A Table Context Selection Framework for Accurate Column Annotations |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,115 |
ST-Raptor: LLM-Powered Semi-Structured Table Question Answering |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,512 |
Auto-Test: Learning Semantic-Domain Constraints for Unsupervised Error Detection in Tables |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,589 |
Birdie: Natural Language-Driven Table Discovery Using Differentiable Search Index |
2025 |
VLDB |
4.1945683e-05 |
| 10,598 |
Auto-Prep: Holistic Prediction of Data Preparation Steps for Self-Service Business Intelligence |
2025 |
VLDB |
4.1945683e-05 |
| 10,675 |
On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing |
2025 |
VLDB |
4.1945683e-05 |
| 10,752 |
QUEST: Query Optimization in Unstructured Document Analysis |
2025 |
VLDB |
4.1945683e-05 |
| 10,753 |
Cents: A Flexible and Cost-Effective Framework for LLM-Based Table Understanding |
2025 |
VLDB |
4.1945683e-05 |
| 10,823 |
TableCopilot: A Table Assistant Empowered by Natural Language Conditional Table Discovery |
2025 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 192 |
HoloClean: Holistic Data Repairs with Probabilistic Inference |
2017 |
VLDB |
0.00035728858 |
| 221 |
Deep Entity Matching with Pre-Trained Language Models |
2021 |
VLDB |
0.00033121824 |
| 300 |
Deep Learning for Entity Matching: A Design Space Exploration |
2018 |
SIGMOD |
0.00028441466 |
| 303 |
Generic Schema Matching with Cupid |
2001 |
VLDB |
0.00028301477 |
| 420 |
InfoGather: Entity Augmentation and Attribute Discovery By Holistic Matching with Web Tables |
2012 |
SIGMOD |
0.00023719065 |
| 513 |
TURL: Table Understanding through Representation Learning |
2021 |
VLDB |
0.00021288342 |
| 517 |
Can Foundation Models Wrangle Your Data? |
2023 |
VLDB |
0.00021169035 |
| 656 |
ERACER: A Database Approach for Statistical Inference and Data Cleaning |
2010 |
SIGMOD |
0.00018588729 |
| 1,317 |
Harvesting Relational Tables from Lists on the Web |
2009 |
VLDB |
0.00012625853 |
| 1,627 |
Data Cleaning: Overview and Emerging Challenges |
2016 |
SIGMOD |
0.00011086905 |
| 1,894 |
Baran: Effective Error Correction via a Unified Context Representation and Transfer Learning |
2020 |
VLDB |
0.0001018378 |
| 2,158 |
Uni-Detect: A Unified Approach to Automated Error Detection in Tables |
2019 |
SIGMOD |
9.4141354e-05 |
| 2,506 |
Auto-Detect: Data-Driven Error Detection in Tables |
2018 |
SIGMOD |
8.6335464e-05 |
| 2,517 |
Annotating Columns with Pre-trained Language Models |
2022 |
SIGMOD |
8.6092139e-05 |
| 2,888 |
Sato: Contextual Semantic Type Detection in Tables |
2020 |
VLDB |
7.9594996e-05 |
| 3,015 |
Chorus: Foundation Models for Unified Data Discovery and Exploration |
2024 |
VLDB |
7.7092391e-05 |
| 3,478 |
Transform-Data-by-Example (TDE): An Extensible Search Engine for Data Transformations |
2018 |
VLDB |
7.054159e-05 |
| 3,735 |
Auto-Join: Joining Tables by Leveraging Transformations |
2017 |
VLDB |
6.8061318e-05 |
| 3,742 |
TEGRA: Table Extraction by Global Record Alignment |
2015 |
SIGMOD |
6.7966898e-05 |
| 3,995 |
How Large Language Models Will Disrupt Data Management |
2023 |
VLDB |
6.5513237e-05 |
| 4,212 |
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration |
2023 |
SIGMOD |
6.3555142e-05 |
| 5,275 |
Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples |
2023 |
VLDB |
5.5905507e-05 |
| 5,434 |
Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples |
2021 |
SIGMOD |
5.5045402e-05 |
| 6,416 |
Synthesizing Type-Detection Logic for Rich Semantic Data Types using Open-source Code |
2018 |
SIGMOD |
5.072267e-05 |
| 6,800 |
DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models |
2024 |
SIGMOD |
4.9231471e-05 |
| 7,807 |
Pollock: A Data Loading Benchmark |
2023 |
VLDB |
4.6457732e-05 |
| 7,838 |
Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes |
2021 |
SIGMOD |
4.6377995e-05 |
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