Fixed-Size Memory for Constant Attention Cost
If the memory component used in the attention operation is defined as a fixed-size variable, the computational cost of performing the attention function will be fixed. By representing keys and values using this fixed-size memory model, the cost remains constant regardless of the sequence length. This foundational concept opens up several alternative ways to design the memory .
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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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General Form of Memory-Based Attention
Fixed-Size Memory for Constant Attention Cost
Multiple Memory Models in Attention
A language model is tasked with processing an extremely long document. How does an attention mechanism that uses a separate, fixed-size memory component to represent context differ from a standard attention mechanism in managing the information from the beginning of the document as it generates new text?
Managing Context in Long-Sequence Generation
Memory Models vs. Efficient Attention for Cache Optimization
Optimizing a Chatbot for Long Conversations
Notation for Key-Value Pairs
Architectural Strategies for Long-Context Processing
Learn After
Fixed-Size Window Memory as a Form of Local Attention
Summary Vectors for Memory Compression in Attention
General Recurrent Formula for Memory Update
Comparison of Memory Storage in Window-based and Moving Average Caches
Hybrid Cache for Attention Mechanisms
An attention mechanism is designed to use a memory component that has a constant, fixed size, regardless of how long the input sequence becomes. What is the primary computational consequence of this design choice as the input sequence length increases significantly?
Computational Cost Scaling in Attention Mechanisms
Optimizing a Real-Time Sequence Processing Model