title: Leave No Context Behind: Efficient Infinite Context Transformers with
    Infini-attention
summary: This work introduces an efficient method to scale Transformer-based 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 Infini-attention. The Infini-attention incorporates a
  compressive memory into the vanilla attention mechanism and builds in both
  masked local attention and long-term linear attention mechanisms in a single
  Transformer block. We demonstrate the effectiveness of our approach on
  long-context 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.
paper_authors._id: 66173d43332ce7fc19839dab
paper_authors.name: Tsendsuren Munkhdalai
paper_authors._id: 66173d43332ce7fc19839dac
paper_authors.name: Manaal Faruqui
paper_authors._id: 66173d43332ce7fc19839dad
paper_authors.name: Siddharth Gopal
paper_title: Leave No Context Behind: Efficient Infinite Context Transformers with
    Infini-attention
paper_summary: This work introduces an efficient method to scale Transformer-based 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 Infini-attention. The Infini-attention incorporates a
  compressive memory into the vanilla attention mechanism and builds in both
  masked local attention and long-term linear attention mechanisms in a single
  Transformer block. We demonstrate the effectiveness of our approach on
  long-context 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.
paper_ai_keywords: Transformer-based Large Language Models (LLMs) Infini-attention compressive memory vanilla attention mechanism masked local attention long-term linear attention mechanisms long-context language modeling benchmarks sequence length passkey context block retrieval book summarization tasks bounded memory parameters fast streaming inference