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Attention Formula in Compressive Transformer
In a multi-memory architecture like the Compressive Transformer, the attention function operates over a unified memory space. To calculate the attention for a specific query , the standard query-key-value mechanism is applied to the concatenation of the local memory () and the compressive memory (). This relationship is mathematically expressed as:
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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
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Evaluating a Dual-Memory Attention Mechanism
A team is developing a language model for processing lengthy legal documents. They use a dual-memory architecture: a 'local memory' that stores the most recent 1024 tokens and a 'compressive memory' that stores a summarized representation of older text. To allow a query (representing a new token) to access information from both recent and long-term history, how should the attention mechanism be structured?
Functional Role of Memory Concatenation in Attention