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Recurrent Memory Models as a Basis for Self-Attention Alternatives
The principles underlying memory models that use recurrent updates, such as the cumulative average method, have served as a foundation for developing more advanced techniques. These advanced methods are being explored as alternatives to the standard self-attention mechanism in Transformers.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
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Recurrent Memory Models as a Basis for Self-Attention Alternatives
Recursive Formula for Memory as a Cumulative Average
A recurrent model with an internal state
his processing a sequence of inputs. The state is updated at each step according to the ruleh_i = f(h_{i-1}, input_i), whereh_{i-1}is the state from the previous step andinput_iis the current input. When the model processes the third input in a sequence, what information does the termh_2(the state after the second input) represent in the computation for the new stateh_3?Analysis of Sequential Information Processing
A neural network processes a sequence of inputs by updating a hidden state
hat each stepiusing the formula:h_i = f(h_{i-1}, input_i). Which component in this formula is directly responsible for carrying forward a compressed summary of the entire sequence processed up to the previous step (i-1)?Recurrent Computation of and in Linear Attention
Real-Time Applications of Recurrent Models
Resurgence of Recurrent Models in Large Language Models
Sequential Token Processing in Recurrent Models
Comparison of Efficient LLM Architectures
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Evaluating a Sequential Memory Mechanism
Consider a simple memory model that processes a sequence of inputs,
input_1, input_2, ..., input_n. It maintains a single memory state,h, which is updated at each stepiby calculating the cumulative average of all inputs seen so far:h_i = (1/i) * sum(input_1 to input_i). How does this update mechanism influence the final memory stateh_nas the sequence lengthnincreases?A sequential processing model needs to maintain a summary of a long stream of numerical inputs. The design requires that more recent inputs have a significantly stronger influence on the final summary than inputs from the distant past. Which of the following state update functions, where
h_iis the state at stepiandinput_iis the current input, best achieves this goal?A model is designed to process a long sequence of information by reading one element at a time and updating a single, continuous memory state. The new memory state at each step is calculated as a function of the previous memory state and the current input element. What is a fundamental limitation of this processing method for tasks requiring an understanding of relationships across the entire sequence?