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.

PhyloLM 是一种应用系统发育算法于大型语言模型的方法，它探索了它们的微调关系并预测其性能特征。通过利用系统发育距离度量，我们构建了树状图，满意地捕捉到不同 LLM 族群（包括 77 个开源和 22 个闭源模型）。此外，系统发育距离能够预测基准测试中的性能（我们测试了 MMLU 和 ARC），从而实现对 LLM 能力的时间和成本效益估计。该方法将遗传概念转化为机器学习，为推断 LLM 的发展、关系和能力提供了工具，即使在透明训练信息缺失的情况下。