Learn Before
Example of a T5 Simplification Task Sample
The T5 text-to-text framework can be adapted for text simplification tasks by formatting both the input and target output as text strings. This is achieved by providing the model with an input string that includes a task-specific prefix, such as 'Simplify:', followed by the complex text. The model is then trained to generate the simplified version. A complete example of this format is: [CLS] Simplify: the professor, who has published numerous papers in his field, will be giving a lecture on the topic next week. → ⟨s⟩ The experienced professor will give a lecture next week.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Example of a T5 Machine Translation Training Sample with Special Tokens
Example of a T5 Question-Answering Sample
Example of a T5 Simplification Task Sample
Differentiating Encoder and Decoder Sequences with Start Symbols
Versatility of the T5 Text-to-Text Format
Definition of c_gold
Formula for Input Embedding Composition
A researcher wants to train a model to perform a new task: converting a sentence from passive voice to active voice. Given the passive input sentence 'The cake was eaten by the dog' and the desired active output 'The dog ate the cake', which of the following training samples is correctly structured according to the unified, prefix-based text-to-text format?
Critiquing a Text-to-Text Training Sample
A single text-to-text model is being trained on a dataset containing samples for four different tasks. Each sample's input begins with a prefix that instructs the model on what to do. Match each input sample (Source Text) with the most likely task it is intended for.
Learn After
A researcher is preparing a dataset to train a text-to-text model for simplifying complex sentences. The goal is to make the text easier to understand. Given the complex sentence 'The meteorological forecast predicts significant precipitation' and its simplified version 'It will rain a lot', which of the following options correctly formats this pair as a single training sample for the model?
Constructing a Text Simplification Training Sample
Debugging a Text-to-Text Model's Training Data