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Question 133 MCQ · 2.0 marks
We continue the notation from the previous question. Suppose the randomly assigned weights
lead to the good event, that is, there is an unique perfect matching in G with minimum weight, and
say this minimum weight is r. What can you say about the determinant of Z in this case?
**LLM**
**Section Id :** 64065391701
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**Number of Questions :** 16
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**Section Marks :** 50
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A published solution is not available for this question yet.
Based on the above data, answer the given subquestions.
Concatenate the outputs from both the attention heads, then apply the output projection matrix
Wo to produce the final output of the multi-head attention mechanism. Select the correct result of
this operation.
Based on the above data, answer the given subquestions.
For the given input matrix X and multihead attention output MHA(X), apply a residual connection
and store the result in matrix R, and finally compute the sum of all elements in matrix R.
A published solution is not available for this question yet.
The table presents the **conditional probability distribution** over vocabulary tokens at each
timestep during sequence generation. Each **column** corresponds to a timestep (from 1 to 5), and
the values represent the probability of selecting each token given the tokens chosen in all previous
timesteps. For timestep t, the values in the column represent:
Based on the above data, answer the given subquestions.
In exhaustive search, at timestep t=1, we run the decoder once to obtain probability distributions
over all tokens in the vocabulary. Given the 6 tokens available in the table in the main question,
how many times must we run the decoder at timestep t=4?
A published solution is not available for this question yet.
The table presents the **conditional probability distribution** over vocabulary tokens at each
timestep during sequence generation. Each **column** corresponds to a timestep (from 1 to 5), and
the values represent the probability of selecting each token given the tokens chosen in all previous
timesteps. For timestep t, the values in the column represent:
Based on the above data, answer the given subquestions.
How many total sequences of exactly length 5 are possible according to exhaustive search?
A published solution is not available for this question yet.
The table presents the **conditional probability distribution** over vocabulary tokens at each
timestep during sequence generation. Each **column** corresponds to a timestep (from 1 to 5), and
the values represent the probability of selecting each token given the tokens chosen in all previous
timesteps. For timestep t, the values in the column represent:
Based on the above data, answer the given subquestions.
If we use top-k sampling with k=2 at timestep 1, what is the normalized probability of selecting
token “sky” at the timestep=1?
A published solution is not available for this question yet.
