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ProxyFormer architecture enables ultra-long context in language models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ProxyFormer, a novel dual-stream architecture designed to overcome the computational limitations of processing ultra-long contexts and high-resolution data in language models. The architecture uses proxy tokens to compress fine-grained local features, enabling expensive global interactions to occur in a reduced space. This approach allows for significantly longer sequence lengths to be trained, with one model demonstrating the ability to handle up to 1,048,576 tokens with high retrieval accuracy. ProxyFormer has also shown promise in preliminary image-generation experiments. AI

IMPACT ProxyFormer's architecture could significantly reduce the computational cost of processing long contexts, potentially enabling new applications in areas requiring extensive data analysis.

RANK_REASON The cluster describes a new architecture proposed in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ProxyFormer architecture enables ultra-long context in language models

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The cluster describes a new architecture proposed in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    ProxyFormer: A Dual-Stream Proxy Architecture for Ultra-Long Context and High-Resolution Generation

    The quadratic growth of attention computation and key-value (KV) cache with respect to sequence length is a central bottleneck for ultra-long-context language models and high-resolution generative models. We propose ProxyFormer, a general dual-stream architecture built upon proxy…

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