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Awesome-LLM-Tabular: a curated list of Large Language Model applied to Tabular Data
| Date | Stars |
|---|---|
| 2026-07-31 | 427 |
| 2026-08-02 | 428 |
| 2026-08-06 | 428 |
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# Awesome-LLM-Tabular [](https://github.com/johnnyhwu/Awesome-LLM-Tabular) [](https://opensource.org/licenses/MIT)  :bulb: Since the emergence of ChatGPT, Large Language Models (LLMs) have garnered significant attention, with new advancements continuously emerging. LLMs have found applications in various domains like vision, audio, and text tasks. However, **tabular data** remains a crucial data format in this world. Hence, this repo focuses on collecting research papers that explore the integration of LLM technology with tabular data, and aims to save you valuable time and boost research efficiency. :sparkles: Awesome-LLM-Tabular is a curated list of **Large Language Model** applied to **Tabular Data**. :fire: This project is currently under development. Feel free to :star: (STAR) and :telescope: (WATCH) it to stay updated on the latest developments. ## Table of Content - [Awesome-LLM-Tabular](#awesome-llm-tabular) - [Table of Content](#table-of-content) - [Related Papers](#related-papers) - [Workshops](#workshops) - [Useful Blogs](#useful-blogs) ## Related Papers | Date | keywords | Paper | Publication | Resource | | :--: | :------: | :---: | :---------: | :------: | | 2019/09 | TabFact | [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://arxiv.org/abs/1909.02164) |  | [](https://github.com/wenhuchen/Table-Fact-Checking?tab=readme-ov-file) | | 2020 | TableGPT | [TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching](https://aclanthology.org/2020.coling-main.179.pdf) |  | [](https://github.com/syw1996/TableGPT) | | 2020/05 | TaBERT | [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://arxiv.org/abs/2005.08314) |  | [](https://github.com/facebookresearch/TaBERT) | | 2020/09 | GaPPa | [GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing](https://arxiv.org/abs/2009.13845) |  | [](https://github.com/taoyds/grappa) | | 2020/10 | TabGAN | [Synthetic tabular data generation using GANs (CTGAN), Diffusion Models, and LLMs (GReaT) with adversarial filtering, privacy metrics, and sklearn integration.](https://arxiv.org/abs/2010.00638) | | [](https://github.com/Diyago/Tabular-data-generation) | | 2022/02 | TableQuery | [TableQuery: Querying tabular data with natural language](https://arxiv.org/pdf/2202.00454.pdf) | | | | 2022/05 | FeSTE | [Few-Shot Tabular Data Enrichment Using Fine-Tuned Transformer Architectures](https://aclanthology.org/2022.acl-long.111.pdf) |  | | | 2022/05 | FM | [Can Foundation Models Wrangle Your Data?](https://arxiv.org/abs/2205.09911) |  | [](https://github.com/HazyResearch/fm_data_tasks) | | 2022/05 | TURL | [Technical Perspective of TURL: Table Understanding through Representation Learning](https://dl.acm.org/doi/abs/10.1145/3542700.3542708) |  | | | 2022/06 | TabText | [TabText: A Flexible and Contextual Approach to Tabular Data Representation](https://arxiv.org/abs/2206.10381) | | | | 2022/06 | LIFT
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matched fp:d8fab436aa787c04, topic:awesome, desc:curated list
matched fp:d8fab436aa787c04, topic:large-language-models