Measuring, Localizing, and Ablating Alignment Signatures in LLMs
The research investigates if AI-like styles in aligned language models are just output patterns or internal representation signatures left by post-training alignment. The authors developed PASTA, a training-free method to estimate these signatures from model differences. They showed that this signature can be causally ablated during inference to reduce AI detection rates across multiple detectors, proving the style is a measurable representational effect.