NLP

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Across
  1. 2. This memory-efficient fine-tuning method introduces rank-decomposed matrices to update only a fraction of model parameters.
  2. 3. A dense vector representation of words or tokens used to capture semantic meaning
  3. 6. A neural network architecture that revolutionized NLP by introducing self-attention mechanisms
  4. 7. A transformer model that processes text bidirectionally, making it adept at tasks requiring context from both past and future tokens
  5. 8. A mechanism that helps models focus on relevant parts of the input sequence while processing text
  6. 10. One complete pass through the entire training dataset during the learning process
Down
  1. 1. The input query or text that guides the behavior of a language model to generate desired outputs
  2. 4. The stage where a trained model applies its learned parameters to predict or generate outputs for unseen data
  3. 5. A regularization technique that randomly ignores a subset of neurons during training to prevent overfitting
  4. 9. The process of splitting text into smaller units, often words or subwords, for input to language models