Calibrating Transformer Attention via Task-Space Sensitivity Feedback
Quick summary
arXiv:2512.20661v2 Announce Type: replace Abstract: Transformer-based pre-trained language models (PLMs) excel in text classification but suffer from attention dilution and attention sink effects, forcing models to over-focus on task-irrelevant tokens. Existing attention supervision methods rely on costly token-level human annotations or static heuristics, which fail to scale or capture context-dependent token importance. To address this, we propose AttCal, a self-supervised, annotation-free attention calibration framework via task-space sensitivity feedback. AttCal treats attention distributi
Key takeaways
- arXiv:2512.20661v2 Announce Type: replace Abstract: Transformer-based pre-trained language models (PLMs) excel in text classification but suffer from attention dilution and attention sink effects, forcing models to over-focus on task-irrelevant tokens.
- Existing attention supervision methods rely on costly token-level human annotations or static heuristics, which fail to scale or capture context-dependent token importance.
- To address this, we propose AttCal, a self-supervised, annotation-free attention calibration framework via task-space sensitivity feedback.
Why it matters
“Calibrating Transformer Attention via Task-Space Sensitivity Feedback” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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