Correcting a Misconception in Vector Generation
A fellow student claims that to generate the key vector for a specific token in an input sequence, you must use a different token from that sequence as the input. Identify the fundamental misunderstanding in this claim and explain the correct procedure for generating the query, key, and value vectors for that single token.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Empirical Science
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Single-Step Generation with a KV Cache
Updating the KV Cache
In a self-attention layer processing an input sequence of two tokens, let the input vector for the first token be
x_1and for the second token bex_2. The layer generates a query vectorq_1(for the first token) and a key vectork_2(for the second token). Which statement accurately describes the relationship between these inputs and generated vectors?Correcting a Misconception in Vector Generation
Calculating a Query Vector in Self-Attention
In a standard self-attention mechanism, an input vector is transformed into three separate vectors (Query, Key, and Value) using three distinct, learned weight matrices. Imagine a modified self-attention layer where these three weight matrices are constrained to be identical. What would be the most direct consequence of this change?