Exploring Model Kinship for Merging Large Language Models
Authors: name: Yedi Hu, name: Yunzhi Yao, name: Ningyu Zhang, name: Shumin Deng, name: Huajun Chen

Model merging has become one of the key technologies for enhancing the
capabilities and efficiency of Large Language Models (LLMs). However, our
understanding of the expected performance gains and principles when merging any
two models remains limited. In this work, we introduce model kinship, the
degree of similarity or relatedness between LLMs, analogous to biological
evolution. With comprehensive empirical analysis, we find that there is a
certain relationship between model kinship and the performance gains after
model merging, which can help guide our selection of candidate models. Inspired
by this, we propose a new model merging strategy: Top-k Greedy Merging with
Model Kinship, which can yield better performance on benchmark datasets.
Specifically, we discover that using model kinship as a criterion can assist us
in continuously performing model merging, alleviating the degradation (local
optima) in model evolution, whereas model kinship can serve as a guide to
escape these traps. Code is available at
https://github.com/zjunlp/ModelKinship.