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Awesome-LLM-Tabular
Awesome-LLM-Tabular: a curated list of Large Language Model applied to Tabular Data
https://github.com/johnnyhwu/Awesome-LLM-Tabular
- TabFact: A Large-scale Dataset for Table-based Fact Verification - Fact-Checking?tab=readme-ov-file) |
- TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching
- TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
- GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
- TableQuery: Querying tabular data with natural language
- Few-Shot Tabular Data Enrichment Using Fine-Tuned Transformer Architectures
- Can Foundation Models Wrangle Your Data?
- Technical Perspective of TURL: Table Understanding through Representation Learning
- TabText: A Flexible and Contextual Approach to Tabular Data Representation
- LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks - Madison-Lee-Lab/LanguageInterfacedFineTuning) |
- PTab: Using the Pre-trained Language Model for Modeling Tabular Data
- Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning
- Language Models are Realistic Tabular Data Generators
- TabLLM: Few-shot Classification of Tabular Data with Large Language Models
- IngesTables: Scalable and Efficient Training of LLM-Enabled Tabular Foundation Models
- Elephants Never Forget: Testing Language Models for Memorization of Tabular Data - Tabular-Memorization-Checker) |
- Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
- Adapting Prompt for Few-shot Table-to-Text Generation
- Transformers for Tabular Data Representation: A Survey of Models and Applications
- TABLET: Learning From Instructions For Tabular Data - slack/Tablet) |
- AnyPredict: Foundation Model for Tabular Prediction
- Generative Table Pre-training Empowers Models for Tabular Prediction
- TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
- UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
- TOWARDS FOUNDATION MODELS FOR LEARNING ON TABULAR DATA
- Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs
- UniPredict: Large Language Models are Universal Tabular Classifiers
- Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study
- Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data
- Chain-of-Thought Reasoning in Tabular Language Models
- Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
- TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data - tat) |
- LLM on Tabular Data: Prediction, Generation, and Understanding
- CABINET: Content Relevance based Noise Reduction for Table Question Answering
- OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
- CancerGPT for few shot drug pair synergy prediction using large pretrained language models
- Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study
- Unleashing the Potential of Large Language Models for Predictive Tabular Tasks in Data Science - llm) |
- Making Pre-trained Language Models Great on Tabular Prediction - berta) |
- Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning - Han/FeatLLM) |
- TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition - Han/FeatLLM) |
- LLMClean: Context-Aware Tabular Data Cleaning via LLM-Generated OFDs
- Table Representation Learning Workshop @ NeurIPS 2023
- Table Representation Learning Workshop @ NeurIPS 2024
- A Short Chronology Of Deep Learning For Tabular Data