Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures

TL;DR

This paper investigates the mechanistic similarities between Transformer and Mamba language models, using interpretability techniques to reveal structural and feature-level parallels supporting the Universality hypothesis.

Contribution

It introduces a method to compare features across models using Sparse Autoencoders and demonstrates structural circuit similarities, advancing understanding of model universality.

Findings

Most features are similar across Transformers and Mambas.

Induction circuits are structurally analogous in both models.

Identifies the Off-by-One motif unique to Mamba models.

Abstract

The hypothesis of Universality in interpretability suggests that different neural networks may converge to implement similar algorithms on similar tasks. In this work, we investigate two mainstream architectures for language modeling, namely Transformers and Mambas, to explore the extent of their mechanistic similarity. We propose to use Sparse Autoencoders (SAEs) to isolate interpretable features from these models and show that most features are similar in these two models. We also validate the correlation between feature similarity and Universality. We then delve into the circuit-level analysis of Mamba models and find that the induction circuits in Mamba are structurally analogous to those in Transformers. We also identify a nuanced difference we call \emph{Off-by-One motif}: The information of one token is written into the SSM state in its next position. Whilst interaction between tokens in Transformers does not exhibit such trend.