Mechanics of Long-Context Hybrid Models Part 1.1: From Hybrid Attention to Hybrid Position
The architectural design of Large Language Models (LLMs) is shifting from traditional full-attention-only models to hybrid models, which combine different attention modules to improve long-context efficiency and performance in length extrapolation and context extension.
Key points
- To explain why hybrid models work and how to design them better, we propose Mechanics of Long-Context Hybrid Models.
- We first observe a Seesaw Effect in Context Extension: LA hybrids benefit more from long-context continual pretraining, whereas SWA hybrids perform better under length extrapolation.
- We find that SWA hybrids suffer from a Short-Context Learning Trap, Short-Window Weariness, and Long-Window Laziness, and require extended windows to enhance performance in continual long-context pretraining.
- For LA hybrids, we summarize the Matthew Effect of Hybrid Position Extrapolation and propose Sliding-Window Linear Attention, achieving 16$\times$ training-free length extrapolation while maintaining 100% accuracy on NIAH-SK1 in 64k context length.
Sources (1)
- [1]Mechanics of Long-Context Hybrid Models Part 1.1: From Hybrid Attention to Hybrid PositionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:02 PM
The architectural design of Large Language Models (LLMs) is shifting from traditional full-attention-only models to hybrid models, which combine different attention modules to improve long-context efficiency and performance in length extrapolation and context extension.
To explain why hybrid models work and how to design them better, we propose Mechanics of Long-Context Hybrid Models.
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