Traditional Transformer models and Large Language Models (LLMs) face limitations in context-dependent memory due to their attention mechanisms. These mechanisms lead to high memory consumption and computation time.
Practical Solution: Compressive Memory Systems
Compressive memory systems offer a practical solution by efficiently managing lengthy sequences with constant storage and computation costs. Unlike traditional attention mechanisms, they maintain a fixed number of parameters for storing and retrieving information, reducing memory expansion with input sequence length.
Google’s Unique Solution: Infini-attention
Google’s researchers have proposed Infini-attention, a unique attention mechanism that combines long-term linear attention and masked local attention into a single Transformer block. This approach includes compressive memory in the attention process, effectively managing memory while processing lengthy sequences.
Value and Applications
The Infini-attention method has shown effectiveness in tasks such as book summarizing and language modeling with input sequences of up to 1 million tokens. It enables minimal bounded memory parameters and fast streaming inference for real-time analysis of sequential input.
Key Contributions
The team presents Infini-attention as a useful method that represents contextual dependencies over short and long distances. It can be easily incorporated into current Transformer structures, enabling continuous pre-training and long-context adaptation.
Conclusion
This research is a significant advancement for Large Language Models, enabling efficient handling of very long inputs in terms of computation and memory utilization.
For further details, refer to the paper.
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