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.
Categories: cs.CL