arXiv Machine Learning
Sep 11

Perturbation: A simple and efficient adversarial tracer for representation learning in language models

The paper introduces Perturbation, a method that treats representations in language models as learning conduits rather than activation patterns. By fine‑tuning a model on a single adversarial example and observing how this perturbation spreads to other inputs, the approach avoids geometric assumptions and does not identify representations in untrained models. In trained models, Perturbation uncovers structured transfer across multiple linguistic scales, indicating that language models generalize along representational lines and acquire linguistic abstractions through experience.

By Joshua Rozner, Cory Shain
arXiv Machine Learning
Oct 8

Learning Cross-Model Activation Alignments with Explicit Many-to-Many Layer Maps

The paper introduces MATCHA, a method that learns explicit many-to-many layer maps between independently trained language models. MATCHA decomposes the alignment into a layer map—an explicit target‑by‑source matrix—and a shared feature map, allowing joint learning of layer correspondence and feature transformation from prompts. Experiments on 42 model pairs across three families show that MATCHA reconstructs target activations more faithfully and improves retrieval metrics, while enabling transfer of activation‑space interventions between models.

By Alina Sudakov, Guy Bar-Shalom, Fabrizio Frasca, Haggai Maron