mirror of
https://github.com/dair-ai/Prompt-Engineering-Guide
synced 2024-11-08 07:10:41 +00:00
26 lines
1.4 KiB
Plaintext
26 lines
1.4 KiB
Plaintext
# Efficient Infinite Context Transformers
|
|
|
|
import {Bleed} from 'nextra-theme-docs'
|
|
|
|
<iframe width="100%"
|
|
height="415px"
|
|
src="https://www.youtube.com/embed/tOaTaQ8ZGRo?si=pFP-KiLe63Ppl9Pd" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
|
|
allowFullScreen
|
|
/>
|
|
|
|
A new [paper](https://arxiv.org/abs/2404.07143) by Google integrates compressive memory into a vanilla dot-product attention layer.
|
|
|
|
The goal is to enable Transformer LLMs to effectively process infinitely long inputs with bounded memory footprint and computation.
|
|
|
|
They propose a new attention technique called Infini-attention which incorporates a compressive memory module into a vanilla attention mechanism.
|
|
|
|
|
|
|
|
It builds in both masked local attention and long-term linear attention into a single Transformer block. This allows the Infini-Transformer model to efficiently handle both long and short-range contextual dependencies.
|
|
|
|
This approach outperforms baseline models on long-context language modeling with a 114x compression ratio of memory!
|
|
|
|
They also show that a 1B LLM can naturally scale to a 1M sequence length and a 8B model achieves a new SoTA result on a 500K length book summarization task.
|
|
|
|
Given how important long-context LLMs are becoming having an effective memory system could unlock powerful reasoning, planning, continual adaption, and capabilities not seen before in LLMs.
|