Combining Multiple Corruption Methods in Pre-training
To enhance model robustness during pre-training, it is a common strategy to utilize a combination of different input corruption techniques. This can be implemented by randomly selecting one of the available corruption methods for each individual training instance.
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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
Token Masking as an Input Corruption Method
Token Deletion as an Input Corruption Method
Combining Multiple Corruption Methods in Pre-training
Selecting Appropriate Input Corruption Methods
Token Alteration as an Input Corruption Method
Token Reordering as an Input Corruption Method
Input Corruption Methods for Multi-Sentence Sequences
Input Corruption Methods for Multi-Sentence Sequences
Corruption Methods for Multi-Sentence Sequences
A research team is pre-training an encoder-decoder model using a denoising objective. Their primary goal is to create a model that excels at summarizing long documents, which requires a deep understanding of the text's overall semantic content and logical flow, rather than its exact word-for-word structure. Which of the following input corruption strategies would be most aligned with this specific goal?
You are training an encoder-decoder model with a denoising objective. Match each input corruption method with the primary linguistic capability it is designed to teach the model.
Diagnosing Pre-training Deficiencies
Learn After
A research team aims to pre-train a language model to be highly robust against a wide variety of real-world text errors, including typos, missing words, and jumbled phrases. Which of the following input corruption strategies during pre-training is most likely to achieve this goal of general robustness?
Rationale for Mixed Corruption Strategies in Pre-training
Evaluating a Pre-training Strategy for a Code Generation Model