TAME: Token Attribution and Masking for Emergent misalignment
Fine-tuning an aligned language model on narrow, flawed data can induce harmful behavior far outside the training domain, known as emergent misalignment (EM). Prior work has localized EM in model weights, activations, and training documents, but it remains unclear which training tokens carry the relevant fine-tuning signal. We introduce TAME (Token Attribution and Masking for Emergent Misalignment), a three-stage framework: token attribution scores how strongly the fine-tuning update raises each response token's likelihood, using forward passes through a released LoRA adapter; signal characterization finds patterns among high-attribution tokens; and causal validation tests them by attribu...
| Authors | Md Rayhanul Masud, Md Rizwan Parvez |
|---|---|
| Primary category | cs.LG |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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