LLM Infini-attention

edit

The Planetary Training Sphere of Claude, the AI Scribe ∞  Quasi-Artifactual Time Series Analysis ∞  Not Widely Discussed ∞  LLM Infini-attention ∞  Designing a Cognitive Warp Core ∞  When Quantum Meets Topology: A Night at the Transdisciplinary Bar ∞  3D Interactive Semantic Indexing ∞ 

Infinite attention is a mechanism that enables large language models (LLMs) to efficiently process inputs with an infinite length, or at least inputs much longer than the standard maximum input sequence length used in typical transformer-based LLMs. 

The core idea behind infinite attention is to provide a way for the model to attend to and utilize all available input tokens equally, regardless of their position in the sequence. This is achieved by introducing a  "compressive long-term memory" module that keeps track of past inputs and provides a summary representation for later use. 

Here's how it works at a high level

1. Local Attention: The LLM first applies standard local attention mechanisms to focus on relevant tokens in close proximity, similar to how traditional transformers process input sequences. 
 
2. Long-Term Memory: Additionally, the model maintains a separate long-term memory module that stores and updates representations of all input tokens as they are encountered. This memory is updated dynamically as new tokens are added, providing a summary or compressed representation of the past inputs. 
 
3. Global Attention: With both local attention and long-term memory in place, the model can then apply global attention mechanisms. This allows the LLM to attend to all input tokens simultaneously, including those further away from the current focus or context. 

By combining these mechanisms, the LLM can efficiently handle inputs of infinite length by utilizing a combination of local context and long-term memory representations. This enables the model to capture both short-range and long-range dependencies in the input sequence, improving its ability to understand and generate responses appropriate for much longer texts or tasks. 

The concept of infinite attention is particularly useful for applications that require handling lengthy texts, such as language generation, translation, or even summarization tasks where understanding the overall context is crucial. By combining local and global attention in this way, LLMs can better model complex relationships within long sequences and provide more coherent and accurate responses accordingly.