Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·10d ago·5 min read
Auditing Routing Entropy as an Uncertainty Signal in Attention-Residual Transformers
TL;DR
Routing entropy in Attention-Residual Transformers was audited to assess its reliability as an uncertainty signal. Results indicate that while routing entropy provides some information, it does not significantly enhance prediction accuracy beyond model confidence alone.
✦ Why It Matters
Consider integrating routing entropy analysis in your model evaluation to better understand prediction reliability.
Key Takeaways
- Audit routing entropy to assess prediction reliability.
- Use soft-binned calibration for improved model training.
- Evaluate the effectiveness of routing signals in your models.
- Be cautious in interpreting routing information as a correctness indicator.
Full Summary
Dynamic architectures like Attention-Residual Transformers leave a routing trace for each prediction, which can indicate prediction reliability. This study examined routing entropy in Swin-Tiny and DeiT-Small models trained on CIFAR-10/100 datasets, using a soft-binned calibration loss.
Three tests were conducted to evaluate whether routing signals provide additional correctness information beyond model confidence. Results showed that no significant improvements were found in routing-stratified calibration, and the entropy-profile probe performed better than shuffled profiles but worse than confidence-only predictors.
Specifically, at an injected effect of 0.010 nats, the profile probe recovered only 24-59% of the oracle gain, suggesting limited utility of routing entropy as a standalone uncertainty signal. These findings highlight the need for caution in interpreting routing information as a reliable indicator of prediction correctness.