  Do architectural differences significantly affect the way models represent
and process language? We propose a new approach, based on metric-learning
encoding models (MLEMs), as a first step to answer this question. The approach
provides a feature-based comparison of how any two layers of any two models
represent linguistic information. We apply the method to BERT, GPT-2 and Mamba.
Unlike previous methods, MLEMs offer a transparent comparison, by identifying
the specific linguistic features responsible for similarities and differences.
More generally, the method uses formal, symbolic descriptions of a domain, and
use these to compare neural representations. As such, the approach can
straightforwardly be extended to other domains, such as speech and vision, and
to other neural systems, including human brains.
