Unleashing the Potentials of Likelihood Composition for Multi-modal Language Models
<|reference_start|>Unleashing the Potentials of Likelihood Composition for Multi-modal Language Models: Model fusing has always been an important topic, especially in an era where large language models (LLM) and multi-modal language models (MLM) with different architectures, parameter sizes and training pipelines, are being created all the time. In this work, we propose a post-hoc framework, aiming at fusing heterogeneous models off-the-shell, which we call \textit{likelihood composition}, and the basic idea is to compose multiple models' likelihood distribution when doing a multi-choice visual-question-answering task. Here the core concept, \textit{likelihood}, is actually the log-probability of the candidate answer. In \textit{likelihood composition}, we introduce some basic operations: \textit{debias}, \textit{highlight}, \textit{majority-vote} and \textit{ensemble}. By combining (composing) these basic elements, we get the mixed composition methods: \textit{mix-composition}. Through conducting comprehensive experiments on 9 VQA datasets and 10 MLMs, we prove the effectiveness of \textit{mix-composition} compared with simple \textit{ensemble} or \textit{majority-vote} methods. In this framework, people can propose new basic composition methods and combine them to get the new mixed composition methods. We hope our proposed \textit{likelihood composition} can provide a new perspective of fusing heterogeneous models and inspire the exploration under this framework.<|reference_end|>
@article{zhao2024unleashing,
    title={Unleashing the Potentials of Likelihood Composition for Multi-modal
  Language Models},
    author={Shitian Zhao, Renrui Zhang, Xu Luo, Yan Wang, Shanghang Zhang, Peng
  Gao},
    journal={arXiv preprint arXiv:2410.00363},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00363},
    primaryClass={cs.CL}
}