Awesome-LLM-Tabular

💡 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.

✨ Awesome-LLM-Tabular is a curated list of Large Language Model applied to Tabular Data.

🔥 This project is currently under development. Feel free to ⭐ (STAR) and 🔭 (WATCH) it to stay updated on the latest developments.

Table of Content

• Awesome-LLM-Tabular
  • Table of Content
  • Related Papers

Related Papers

Date	keywords	Paper	Summary	Publication	Resource
2020	TableGPT	TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching
2020-05	TaBERT	TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
2022-02	TableQuery	TableQuery: Querying tabular data with natural language
2022-05	FeSTE	Few-Shot Tabular Data Enrichment Using Fine-Tuned Transformer Architectures
2022-05	FM	Can Foundation Models Wrangle Your Data?
2022-06	TabText	TabText: A Flexible and Contextual Approach to Tabular Data Representation
2022-06	LIFT	LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks
2022-09	PTab	PTab: Using the Pre-trained Language Model for Modeling Tabular Data
2022-10	GReaT	Language Models are Realistic Tabular Data Generators
2022-10	TabLLM	TabLLM: Few-shot Classification of Tabular Data with Large Language Models
2023-04	TABLET	TABLET: Learning From Instructions For Tabular Data
2023-05	AnyPredict	AnyPredict: Foundation Model for Tabular Prediction
2023-07	TableGPT	TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
2023-10	TabFMs	Towards Foundation Models for Learning on Tabular Data

Contributing

We welcome contributions to keep this repository up-to-date with the latest research and applications of LLM in the tabular domain. Whether you want to correct any mistakes, add new content, or suggest improvements, your contributions are highly appreciated 🤗.