Poster
in
Workshop: UniReps: Unifying Representations in Neural Models
Towards Measuring Representational Similarity of Large Language Models
Max Klabunde · Mehdi Ben Amor · Michael Granitzer · Florian Lemmerich
[
Abstract
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presentation:
UniReps: Unifying Representations in Neural Models
Fri 15 Dec 6:15 a.m. PST — 3:15 p.m. PST
[
OpenReview]
Fri 15 Dec 6:15 a.m. PST — 3:15 p.m. PST
Abstract:
Understanding the similarity of the numerous large language models released 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.
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