Towards Measuring Representational Similarity of Large Language Models
Max Klabunde Mehdi Ben Amor Michael Granitzer Florian Lemmerich
Understanding the similarity of the numerous released large language models
(LLMs) has many uses, e.g., simplifying model selection, detecting illegal
model reuse, and advancing our understanding of what makes LLMs perform well.
In this work, we measure the similarity of representations of a set of LLMs
with 7B parameters. Our results suggest that some LLMs are substantially
different from others. We identify challenges of using representational
similarity measures that suggest the need of careful study of similarity scores
to avoid false conclusions.