The paper "Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks" addresses the limitations of current language models, such as GPT-3 and ChatGPT, in understanding and performing tasks on relational tables. These models, primarily trained on one-dimensional natural language texts, struggle with two-dimensional table structures, which are crucial for many common table-related tasks. To address this, the authors propose a new paradigm called "table fine-tuning," where language models are fine-tuned using diverse table tasks synthesized from real tables. This approach aims to enhance the models' ability to understand and perform tasks on tables, similar to how instruction fine-tuning improves models' ability to follow human instructions. The authors conduct extensive experiments to demonstrate the effectiveness of their TABLE-GPT models. They show that TABLE-GPT outperforms vanilla GPT-3.5 and ChatGPT on a wide range of table tasks, including data transformation, data cleaning, data profiling, data imputation, and table question-answering (table-QA). The models also exhibit strong generalizability, performing well on new and unseen table tasks. The paper includes detailed descriptions of the table-tuning process, including the synthesis and augmentation of training data, and provides a comprehensive evaluation benchmark for future research. Key contributions of the work include: 1. Proposing a new "table-tuning" paradigm to enhance language models' ability to understand tables. 2. Developing task-level, table-level, instruction-level, and completion-level data augmentation techniques to avoid overfitting and ensure generalizability. 3. Showcasing that TABLE-GPT excels on both seen and unseen table tasks and can serve as a foundation model for downstream optimizations. The authors release their code, training data, and evaluation benchmark to facilitate further research in this area.The paper "Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks" addresses the limitations of current language models, such as GPT-3 and ChatGPT, in understanding and performing tasks on relational tables. These models, primarily trained on one-dimensional natural language texts, struggle with two-dimensional table structures, which are crucial for many common table-related tasks. To address this, the authors propose a new paradigm called "table fine-tuning," where language models are fine-tuned using diverse table tasks synthesized from real tables. This approach aims to enhance the models' ability to understand and perform tasks on tables, similar to how instruction fine-tuning improves models' ability to follow human instructions. The authors conduct extensive experiments to demonstrate the effectiveness of their TABLE-GPT models. They show that TABLE-GPT outperforms vanilla GPT-3.5 and ChatGPT on a wide range of table tasks, including data transformation, data cleaning, data profiling, data imputation, and table question-answering (table-QA). The models also exhibit strong generalizability, performing well on new and unseen table tasks. The paper includes detailed descriptions of the table-tuning process, including the synthesis and augmentation of training data, and provides a comprehensive evaluation benchmark for future research. Key contributions of the work include: 1. Proposing a new "table-tuning" paradigm to enhance language models' ability to understand tables. 2. Developing task-level, table-level, instruction-level, and completion-level data augmentation techniques to avoid overfitting and ensure generalizability. 3. Showcasing that TABLE-GPT excels on both seen and unseen table tasks and can serve as a foundation model for downstream optimizations. The authors release their code, training data, and evaluation benchmark to facilitate further research in this area.