Google researchers achieve supposedly infinite context attention via compressive memory.

Paper: https://arxiv.org/abs/2404.07143

Abstract:
This work introduces an efficient method to scale Transformerbased Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infiniattention. The Infiniattention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and longterm linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on longcontext language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs.

Authors: Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal

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