Secure Linear Alignment of Large Language Models

This paper shows that different language models learn surprisingly similar internal representations, even when trained differently. The researchers exploit this similarity to create a privacy-preserving system where one model can use another model's knowledge through a simple mathematical transformation (affine alignment), without sharing raw data or model weights—using encryption to keep queries secure.