PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks
<|reference_start|>PhyloLM : Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks: 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.<|reference_end|>
@article{yax2024phylolm,
    title={PhyloLM : Inferring the Phylogeny of Large Language Models and
  Predicting their Performances in Benchmarks},
    author={Nicolas Yax, Pierre-Yves Oudeyer, Stefano Palminteri},
    journal={arXiv preprint arXiv:2404.04671},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04671},
    primaryClass={cs.CL cs.LG q-bio.PE}
}