A.Explicitly providing k examples of the intended task in the prompt to guide the models output

B.The process of training the model on k different tasks simultaneously to improve its versatility

C.Providing the exact k words in the prompt to guide the model's response

D.Limiting the model to only k possible outcomes or answers for a given task

A.Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.

B.Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.

C.Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.

D.Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

A.Top p assigns penalties to frequently occurring tokens.

B.Top p limits token selection based on the sum of their probabilities.

C.Top p selects tokens from the "Top k' tokens sorted by probability.

D.Top p determines the maximum number of tokens per response.

A.Updates the weights of the base model during the fine-tuning process

B.Evaluates the performance metrics of the custom model

C.Hosts the training data for fine-tuning custom model

D.Serves as a designated point for user requests and model responses