Learn Before
Explicit Context Encoding via Additional Memory Models
To address the growing cost of caching representations in global Transformer models, researchers explore explicitly encoding the context via an additional memory model. This approach serves as an alternative or complementary idea to optimizing the Key-Value (KV) cache through efficient attention mechanisms like sparse and linear attention.
0
1
Tags
Foundations of Large Language Models
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Key-Value (KV) Cache in Transformer Inference
A language model using a standard Transformer architecture is generating a long sequence of text one token at a time. How does the computational effort required to generate the 500th token compare to the effort required for the 10th token?
Diagnosing Memory Issues in a Language Model
Difficulty of Training Transformers on Long Sequences
Evaluating Context Handling in Language Models
Explicit Context Encoding via Additional Memory Models