Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models
<|reference_start|>Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models: The remarkable success of Large Language Models (LLMs) has ushered natural language processing (NLP) research into a new era. Despite their diverse capabilities, LLMs trained on different corpora exhibit varying strengths and weaknesses, leading to challenges in maximizing their overall efficiency and versatility. To address these challenges, recent studies have explored collaborative strategies for LLMs. This paper provides a comprehensive overview of this emerging research area, highlighting the motivation behind such collaborations. Specifically, we categorize collaborative strategies into three primary approaches: Merging, Ensemble, and Cooperation. Merging involves integrating multiple LLMs in the parameter space. Ensemble combines the outputs of various LLMs. Cooperation} leverages different LLMs to allow full play to their diverse capabilities for specific tasks. We provide in-depth introductions to these methods from different perspectives and discuss their potential applications. Additionally, we outline future research directions, hoping this work will catalyze further studies on LLM collaborations and paving the way for advanced NLP applications.<|reference_end|>
@article{lu2024merge,,
    title={Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in
  the Era of Large Language Models},
    author={Jinliang Lu, Ziliang Pang, Min Xiao, Yaochen Zhu, Rui Xia, Jiajun
  Zhang},
    journal={arXiv preprint arXiv:2407.06089},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06089},
    primaryClass={cs.CL}
}