Reformulating a Language Task
Consider a grammatical error correction task. A traditional system might take the input sentence "He have two cat." and produce a structured output like [{"error": "have", "correction": "has"}, {"error": "cat", "correction": "cats"}]. Your goal is to solve this same task using a model that can only process a single string of text as input and can only generate a single string of text as output. Describe a plausible format for the model's output that would successfully convey the corrections within this text-only constraint.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
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Text-to-Text Framework for NLP
Reformulating a Language Task
A research team wants to use a single encoder-decoder model to perform grammatical error correction. How should they formulate this task to fit the text-to-text framework, where both the input and output are text sequences?
An engineer is using a single, versatile model for several different language processing jobs. To do this, they must frame each job as a problem where the model receives an input text and must generate a corresponding output text. Match each job with its correct input/output text formulation.