PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks

• URL: http://arxiv.org/abs/2404.04671v3
• Date: Sun, 16 Jun 2024 14:39:20 GMT
• Title: PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks
• Authors: Nicolas Yax, Pierre-Yves Oudeyer, Stefano Palminteri,
• Abstract summary: This paper introduces PhyloLM, a method adapting phylogenetic algorithms to Large Language Models (LLMs) Our method calculates a phylogenetic distance metrics based on the similarity of LLMs' output. Our phylogenetic distance predicts performance in standard benchmarks, thus demonstrating its functional validity.
• Score: 17.91379291654773
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces PhyloLM, a method adapting phylogenetic algorithms to Large Language Models (LLMs) to explore whether and how they relate to each other and to predict their performance characteristics. Our method calculates a phylogenetic distance metrics based on the similarity of LLMs' output. The resulting metric is then used to construct dendrograms, which satisfactorily capture known relationships across a set of 111 open-source and 45 closed models. Furthermore, our phylogenetic distance predicts performance in standard benchmarks, thus demonstrating its functional validity and paving the way for a time and cost-effective estimation of LLM capabilities. To sum up, by translating population genetic concepts to machine learning, we propose and validate a tool to evaluate LLM development, relationships and capabilities, even in the absence of transparent training information.

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