Explain the concept of self-attention
This tests your ability to explain the core mechanism of Transformers. A strong answer defines self-attention as a process for relating positions of a single sequence, explains the Query-Key-Value (QKV) model where a token's Query is compared to all Keys to generate weights, and describes how these weights create a weighted sum of Values. A red flag is vaguely describing 'importance' without mentioning the QKV mechanism.
This question tests your ability to concisely explain the core mechanism behind the Transformer architecture. A great answer starts by defining self-attention as a method for a model to weigh the importance of all other tokens in a single input sequence when processing any given token. You should then outline the Query-Key-Value (QKV) process: for each token, three vectors are created. The token's Query vector is compared against every other token's Key vector to calculate attention scores. These scores are then used as weights to compute a weighted sum of all Value vectors, producing the final output for that token. A common red flag is only giving a high-level definition of 'weighing importance' without detailing the underlying QKV mechanism.
Read the original → Wikipedia: Attention (machine learning)
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