With the recent developments in the field of Artificial intelligence, Large Language Models, including GPT and LLaMa, are continuously showing remarkable performance over a broad spectrum of natural language tasks. These models have been proven effective in various domains and have advanced the field of Natural Language processing to a great extent. Language models are capable of taking directions from humans and carrying out different jobs. However, there comes a drawback, which is that these models have difficulty with tasks involving the knowledge of tables. This is because their primary training is one-dimensional natural language texts, whereas tables are two-dimensional structures, which accounts for this constraint.
To address this issue, a team of researchers has proposed the concept of table-tuning, an innovative way to alleviate this issue. This method entails further training or optimizing pre-existing language models, such as GPT-3.5 and ChatGPT, using a wide range of table-related tasks derived from actual tables. Enhancing these language models’ capacity to understand and manipulate tables is the main objective of table-tuning.
The Table-GPT models, which have been generated through table-tuning, exhibit improved capabilities in understanding tables. These models have consistently outperformed the standard GPT-3.5 and ChatGPT on a wide range of table-based tasks. This means they can more accurately interpret and manipulate tabular data. The Table-GPT models retain a high degree of generalizability even if they are specialized in table jobs. They are able to adjust to new activities involving tables because they can react to a range of human directions with effectiveness. This flexibility is comparable to ChatGPT’s capacity to manage a variety of natural language jobs and the original GPT-3.5.
The primary contributions have been summarized as follows.
- Table-Tuning Paradigm: Table-Tuning paradigm has been introduced, which involves training language models one more time with the express purpose of improving their efficiency in tasks involving tables. It employs a variety of table-based jobs that are synthesized from actual tables using a synthesize-then-augment methodology.
- Data Augmentation approaches: Task-level, table-level, instruction-level, and completion-level data augmentation approaches have been developed at different levels. These methods are essential for maintaining Table-GPT’s generalizability and preventing overfitting. By adding value to the training set, they strengthen the model.
- Performance in Table-Tasks: Out of the box, Table-GPT exhibits exceptional competence in table-based tasks in both zero-shot and few-shot scenarios. This indicates that the model can perform these tasks quite well, even with little in the way of specialized training or examples.
- Table-GPT’s adaptability makes it suitable for use as a table foundation model. When it comes to downstream single-task optimizations such as task-specific fine-tuning and prompt engineering, it can be a better place to start than the vanilla GPT. This demonstrates how useful it is for a variety of purposes outside of table work.
In summary, the suggested table-tuning paradigm provides a way to overcome the difficulty of teaching language models how to use tables. It improves their comprehension of two-dimensional data structures and gives them the tools they need to succeed in a wide range of table-related jobs, both well-known and unknown.
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.
