mirror of
https://github.com/dair-ai/Prompt-Engineering-Guide
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26 lines
1.4 KiB
Plaintext
26 lines
1.4 KiB
Plaintext
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# Efficient Infinite Context Transformers
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import {Bleed} from 'nextra-theme-docs'
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<iframe width="100%"
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height="415px"
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src="https://www.youtube.com/embed/tOaTaQ8ZGRo?si=pFP-KiLe63Ppl9Pd" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowFullScreen
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/>
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A new [paper](https://arxiv.org/abs/2404.07143) by Google integrates compressive memory into a vanilla dot-product attention layer.
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The goal is to enable Transformer LLMs to effectively process infinitely long inputs with bounded memory footprint and computation.
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They propose a new attention technique called Infini-attention which incorporates a compressive memory module into a vanilla attention mechanism.
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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.
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This approach outperforms baseline models on long-context language modeling with a 114x compression ratio of memory!
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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.
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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.
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