Opening the paper…
Deep Learning Practice, Quiz 1
A start-up is building a new language model for a low-resource language with many compound words and complex morphology. They are debating tokenization strategies. Which of the following approaches is most likely to offer the best balance between vocabulary size, handling OOV words, and capturing morphological variants effectively for this scenario?
A start-up is building a new language model for a low-resource language with many compound words and complex morphology. They are debating tokenization strategies. Which of the following approaches is most likely to offer the best balance between vocabulary size, handling OOV words, and capturing morphological variants effectively for this scenario? When fully fine-tuning a large pre-trained Transformer model (e.g., \>1 Billion parameters), which of the following contributes LEAST significantly to the GPU memory bottleneck compared to the others? A research team wants their pre-trained language model to generate more helpful and harmless responses without extensive task-specific dataset collection. They have a collection of prompts and human-preferred responses. Which of the following techniques directly aligns with this goal and data?