Chapter 06.02: Tasks as text-to-text problem
Reformulating various NLP tasks as text-to-text tasks aims to simplify model architectures and improve performance by treating all tasks as instances of generating output text from input text. This approach addresses shortcomings of BERT’s original design, where different tasks required different output layers and training objectives, leading to a complex multitask learning setup. By unifying tasks under a single text-to-text framework, models can be trained more efficiently and generalize better across diverse tasks and domains.