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AI Research
arXiv CS.AI·Jun 9·5 min read
Ablation-Reversible Heads Don't Transfer: A Stress Test for Mechanistic Role Claims in Transformers
TL;DR
In mechanistic interpretability, claims about the roles of attention heads in transformer models often lack robustness. This study tested the transferability of computations from attention heads in three instruction-tuned models by patching their activations into different prompts.
✦ Why It Matters
Engineers should reconsider the validity of role claims for attention heads in transformer models when designing and interpreting AI systems.
Key Takeaways
- Evaluate attention head roles critically in your models.
- Implement the KID framework for better role assignment.
- Use capability-selective screening to assess head functionality.
- Incorporate specific control checks to validate computations.
How It Works
The KID framework categorizes attention heads based on their roles in computation, using a three-stage process to analyze their effectiveness. Capability-selective screening identifies which heads contribute to specific tasks, while singular value decomposition helps in understanding the structure of their activations.
Activation transduction tests how well these heads can transfer their learned computations to new prompts.