Why Attend to Everything? Focus is the Key
📰 ArXiv cs.AI
Focus, a method that learns which token pairs matter, improves domain perplexity with zero degradation on downstream benchmarks
Action Steps
- Identify token pairs that matter using learnable centroids
- Assign tokens to groups based on centroids
- Restrict distant attention to same-group pairs
- Operate local attention at full resolution
Who Needs to Know This
NLP researchers and AI engineers on a team can benefit from Focus as it allows for more efficient attention mechanisms, while product managers can consider its applications in improving language models
Key Insight
💡 Learning to focus on relevant token pairs can improve model efficiency without degrading performance
Share This
💡 Focus: learn which token pairs matter, not all of them
Key Takeaways
Focus, a method that learns which token pairs matter, improves domain perplexity with zero degradation on downstream benchmarks
Full Article
Title: Why Attend to Everything? Focus is the Key
Abstract:
arXiv:2604.03260v1 Announce Type: cross Abstract: We introduce Focus, a method that learns which token pairs matter rather than approximating all of them. Learnable centroids assign tokens to groups; distant attention is restricted to same-group pairs while local attention operates at full resolution. Because all model weights stay frozen, Focus is purely additive: centroid-only training (as few as 148K parameters) improves domain perplexity with zero degradation on downstream benchmarks--from 1
Abstract:
arXiv:2604.03260v1 Announce Type: cross Abstract: We introduce Focus, a method that learns which token pairs matter rather than approximating all of them. Learnable centroids assign tokens to groups; distant attention is restricted to same-group pairs while local attention operates at full resolution. Because all model weights stay frozen, Focus is purely additive: centroid-only training (as few as 148K parameters) improves domain perplexity with zero degradation on downstream benchmarks--from 1
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