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 {this https URL}.

本研究提出了一种名为 Evolver 的知识融合方法，它可以将不同语言模型的权重进行集成，通过进化算法生成新的模型并与父模型进行评估，达到在不同数据领域通用且性能优越的目的，这种方法与现有模型合并框架无缝集成，为模型增强提供了一种多功能工具。