Authors: Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal
Published on: April 10, 2024
Impact Score: 7.4
Arxiv code: Arxiv:2404.07143
Summary
- What is new: Introduces Infini-attention for scaling LLMs to infinitely long inputs with efficient memory and computation use.
- Why this is important: Scaling Transformer-based LLMs to handle infinitely long inputs with limited memory and computational resources.
- What the research proposes: A novel attention mechanism, Infini-attention, that integrates compressive memory with masked local and long-term linear attention within a single Transformer block.
- Results: Effective handling of long-context benchmarks, achieving successful results on 1M and 500K length tasks with 1B and 8B parameter models.
Technical Details
Technological frameworks used: Transformer-based Large Language Models
Models used: 1B and 8B LLMs
Data used: Long-context language modeling benchmarks, passkey context block retrieval, and book summarization tasks
Potential Impact
Content creation, data analysis, and information retrieval sectors, including companies specializing in AI-driven text analysis, summarization, and long-text processing.
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