  Fine-tuning pre-trained language models, particularly large language models,
demands extensive computing resources and can result in varying performance
outcomes across different domains and datasets. This paper examines the
approach of integrating multiple models from diverse training scenarios into a
unified model. This unified model excels across various data domains and
exhibits the ability to generalize well on out-of-domain data. We propose a
knowledge fusion method named Evolver, inspired by evolutionary algorithms,
which does not need further training or additional training data. Specifically,
our method involves aggregating the weights of different language models into a
population and subsequently generating offspring models through mutation and
crossover operations. These offspring models are then evaluated against their
parents, allowing for the preservation of those models that show enhanced
performance on development datasets. Importantly, our model evolving strategy
can be seamlessly integrated with existing model merging frameworks, offering a
versatile tool for model enhancement. Experimental results on mainstream
language models (i.e., encoder-only, decoder-only, encoder-decoder) reveal that
Evolver outperforms previous state-of-the-art models by large margins. The code
is publicly available at {https://github.com/duguodong7/model-evolution}.
