Fine Tuning is key to building practical AI tools

Introduction

The great thing about instruction tuning, as opposed to fine-tuning, is that you don't train for a particular dataset or distribution; instead, you train for a particular task. As long as the model knows how to perform a task, you can inject whatever taxonomy you have as an example. For instance, if you want to do key information extraction, you don't need to teach the taxonomy to the model; you only need to teach how to do information extraction. The value for x is to extract x, where x can be anything. This scalability is the real value that models like Dum bring. By performing instruction tuning for core tasks such as visual question answering, key information extraction, document classification, and tabular reasoning, the model demonstrates robustness across all tasks and even outperforms the state-of-the-art in several areas.

Keywords

Fine-tuning, Instruction tuning, AI tools, Task-based training, Scalability, Core tasks, Robustness, Dum model, Visual question answering, Information extraction, Document classification, Tabular reasoning

FAQ

1. What is the difference between instruction tuning and fine-tuning? Instruction tuning involves training a model for a specific task without focusing on a particular dataset, while fine-tuning involves adjusting pre-trained models on specific datasets.
2. How does instruction tuning contribute to the scalability of AI models? Instruction tuning allows injecting different taxonomies for various tasks, making the model adaptable to different types of information extraction and enhancing overall scalability.
3. In which core tasks did the model demonstrate robustness after instruction tuning? The model showed robustness in tasks such as visual question answering, key information extraction, document classification, and tabular reasoning, outperforming the state-of-the-art in some areas.