In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?

• A.Selecting a random word from the entire vocabulary at each step
• B.Choosing the word with the highest probability at each step of decoding
• C.Picking a word based on its position in a sentence structure
• D.Using a weighted random selection based on a modulated distribution

Comment: *

Name: *

Email: *

Verification: *

Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

• A.PEFT modifies all parameters and uses unlabeled, task-agnostic data.
• B.PEFT involves only a few or new parameters and uses labeled, task-specific data.
• C.PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
• D.PEFT parameters and b typically used when no training data exists.

Comment: *

Name: *

Email: *

Verification: *

How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

• A.Increasing the temperature flattens the distribution, allowing for more varied word choices.
• B.Increasing the temperature removes the impact of the most likely word.
• C.Temperature has no effect on probability distribution; it only changes the speed of decoding.
• D.Decreasing the temperature broadens the distribution, making less likely words more probable.

Comment: *

Name: *

Email: *

Verification: *

Which statement best describes the role of encoder and decoder models in natural language processing?

• A.Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
• B.Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
• C.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.
• D.Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.

Comment: *

Name: *

Email: *

Verification: *

What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

• A.Overfilling
• B.Underfitting
• C.Data Leakage
• D.Model Drift

Comment: *

Name: *

Email: *

Verification: *