It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
<|reference_start|>It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization: In this paper, we introduce a novel approach for large language model merging via black-box multi-objective optimization algorithms. The goal of model merging is to combine multiple models, each excelling in different tasks, into a single model that outperforms any of the individual source models. However, model merging faces two significant challenges: First, existing methods rely heavily on human intuition and customized strategies to tackle multiple tasks. Second, it's difficult to search for the great model merging configuration in limited evaluations. To address these challenges, we propose a multi-objective optimization based model merging method named MM-MO. The proposed method can automatically search merging configurations for multiple tasks with multi-objective optimization algorithms. Moreover, to obtain high-quality model merging configurations within a limited number of evaluation iterations, we have made several improvements to multi-objective Bayesian optimization specifically for model merging scenarios. First, we introduced a weak-to-strong method to improve the acquisition strategy. Second, we employed Fisher information to select configurations, further increasing the chances of discovering superior model merging configurations. Third, we designed a sparsity metric as an additional optimization objective to enhance the model's generalization performance across different tasks. We conducted comprehensive experiments with other mainstream model merging methods, demonstrating that our method consistently outperforms them. Moreover, performance improvements are observed even on the tasks not explicitly targeted as optimization objectives, indicating that our method enhances the overall potential of the model. ...<|reference_end|>
@article{li2024it's,
    title={It's Morphing Time: Unleashing the Potential of Multiple LLMs via
  Multi-objective Optimization},
    author={Bingdong Li, Zixiang Di, Yanting Yang, Hong Qian, Peng Yang, Hao Hao,
  Ke Tang, Aimin Zhou},
    journal={arXiv preprint arXiv:2407.00487},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00487},
    primaryClass={cs.CL}
}