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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 3·5 min read
Language Models Can Control Their Own Attention
TL;DR
Declarative Attention (DA) allows language models to selectively focus on relevant context during token generation, significantly reducing the number of tokens attended to while maintaining accuracy. This approach can cut down token processing by over 50% in long-context tasks.
✦ Why It Matters
Implement Declarative Attention in your models to enhance efficiency in processing long contexts today.
Key Takeaways
- Explore implementing Declarative Attention in your language models.
- Utilize the three attention modes: global, focus, and local.
- Evaluate the trade-off between token reduction and accuracy.
- Consider scaling models to minimize accuracy loss with DA.
How It Works
Declarative Attention (DA) allows models to declare their attention needs, which the inference engine interprets to optimize token processing. By categorizing attention into global, focus, and local modes, DA reduces the need to read the entire KV cache, thus streamlining the generation process.