PhyloLM: Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks
Keywords: large language models, phylogeny, benchmark
TL;DR: This paper introduces PhyloLM, a method that adapts phylogenetic algorithms to Large Language Models to analyze their relationships and predict performance in benchmarks.
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 metric 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.
Primary Area: interpretability and explainable AI
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Submission Number: 9589
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