Causal Attention Input Structure
In a causal or autoregressive attention mechanism, the input for a given position is composed of the query vector for that specific position, , along with the key and value matrices that contain information from the beginning of the sequence up to and including position . These historical matrices are often denoted as and . For instance, when calculating attention for a token at a position denoted as , the query is represented as , and the corresponding key and value matrices, K and V, encompass all key-value pairs generated up to that point. This structure ensures that the model's output at any step is only influenced by past and present information, adhering to the causal constraint.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.5 Inference - Foundations of Large Language Models
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Causal Attention Input Structure
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Causal Attention Input Structure
An autoregressive model processes an input sequence of 5 tokens, indexed 0 through 4. When calculating the output for the token at index 3, the attention mechanism needs to access a specific set of 'value' vectors from the sequence. Which of the following correctly describes the collection of value vectors available to the query at index 3?
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An autoregressive model processes an input sequence one token at a time. At each position
i, it constructs a matrix containing all value vectors from the beginning of the sequence up to and including positioni. Arrange the matrices below in the order they would be constructed as the model processes the first three positions (indexed 0, 1, and 2).Causal Attention Input Structure
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An engineer is implementing an attention mechanism where the output is a weighted sum of Value vectors, with weights determined by a Softmax function applied to scores. They observe that as the dimension (
d) of the Query and Key vectors increases, the attention weights become extremely concentrated on a single position (e.g.,[0.01, 0.98, 0.01]), causing training instability. The scores are derived from the dot product of Query (Q) and Key (K) matrices. What is the most likely cause of thisAttention Mechanism Misapplication in Summarization
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An autoregressive model is generating a sequence of tokens one by one. It is currently calculating the attention output for the token at position 4 (i.e., the fifth token in the sequence). To ensure the model only uses information it has already seen, which set of key (K) and value (V) vectors must be used as input to the attention mechanism for the query vector at position 4 (q₄)?
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When calculating the attention output for a specific token at position
iin an autoregressive model, the mechanism is structured to use the query vector from that same position (q_i), while the key and value matrices are composed of the corresponding vectors from all positions in the full input sequence.