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Related Questions
- How do pre-trained weights allow large language models to specialize in specific text classification tasks without requiring a costly and time-consuming retraining?
- In what ways does transfer learning permit the adaptation of deep language models to new categories within the same domain by sharing weights?
- What is the distinction between the weights learned early and later in the layer graph for large language models employed for transfer learning?
- How may training examples from one application scenario support learning in later categories due to the overlapping aspects shared across tasks
- What roles may context-dependent and absolute patterns assume in the transformation functions from pre-trained parameters by domain?
- Under what conditions must some hidden units be refinetted, while transferring domain parameters from one use space of language to many of those other use-cases to ensure the output
- How precisely a smaller training set containing mostly generic knowledge can accelerate adaptivity for a transfer leaning network on a similar NLP task
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