  Semantic communication is a promising technology for next-generation wireless
networks. However, the out-of-distribution (OOD) problem, where a pre-trained
machine learning (ML) model is applied to unseen tasks that are outside the
distribution of its training data, may compromise the integrity of semantic
compression. This paper explores the use of multi-modal large language models
(MLLMs) to address the OOD issue in image semantic communication. We propose a
novel "Plan A - Plan B" framework that leverages the broad knowledge and strong
generalization ability of an MLLM to assist a conventional ML model when the
latter encounters an OOD input in the semantic encoding process. Furthermore,
we propose a Bayesian optimization scheme that reshapes the probability
distribution of the MLLM's inference process based on the contextual
information of the image. The optimization scheme significantly enhances the
MLLM's performance in semantic compression by 1) filtering out irrelevant
vocabulary in the original MLLM output; and 2) using contextual similarities
between prospective answers of the MLLM and the background information as prior
knowledge to modify the MLLM's probability distribution during inference.
Further, at the receiver side of the communication system, we put forth a
"generate-criticize" framework that utilizes the cooperation of multiple MLLMs
to enhance the reliability of image reconstruction.
