Inferring the Phylogeny of Large Language Models and Predicting their
  Performances in Benchmarks
Nicolas Yax Pierre-Yves Oudeyer Stefano Palminteri
This paper introduces PhyloLM, a method applying phylogenetic algorithms to
Large Language Models to explore their finetuning relationships, and predict
their performance characteristics. By leveraging the phylogenetic distance
metric, we construct dendrograms, which satisfactorily capture distinct LLM
families (across a set of 77 open-source and 22 closed models). Furthermore,
phylogenetic distance predicts performances in benchmarks (we test MMLU and
ARC), thus enabling a time and cost-effective estimation of LLM capabilities.
The approach translates genetic concepts to machine learning, offering tools to
infer LLM development, relationships, and capabilities, even in the absence of
transparent training information.