Towards Measuring Representational Similarity of Large Language Models

• URL: http://arxiv.org/abs/2312.02730v1
• Date: Tue, 5 Dec 2023 12:48:04 GMT
• Title: Towards Measuring Representational Similarity of Large Language Models
• Authors: Max Klabunde, Mehdi Ben Amor, Michael Granitzer, Florian Lemmerich
• Abstract summary: We measure the similarity of representations of a set of large language models 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.
• Score: 1.7228514699394508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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